An overview of corrosion defect characterization using active infrared thermography

Infrared Physics and Technology 96 (2019) 366–389

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Infrared Physics & Technology journal homepage: www.elsevier.com/locate/infrared

Review

An overview of corrosion defect characterization using active infrared thermography

T

Siavash Doshvarpassanda, , Changzhi Wub, Xiangyu Wangb ⁎

a b

Australasian Joint Research Center for Building Information Modelling & WA School of Mines, Curtin University, Bentley, Western Australia 6102, Australia Australasian Joint Research Center for Building Information Modelling, Curtin University, Bentley, Western Australia 6102, Australia

ARTICLE INFO

ABSTRACT

Keywords: Structural health monitoring Infrared thermography Non-destructive testing Corrosion monitoring Image processing

Corrosion is considered a destructive phenomenon that affects almost all metals. There is a variety of corrosion monitoring and measurement techniques being deployed across industries. However, very few techniques are ideally characterised with non-contact, non-intrusive, on-line and direct features for measuring the accurate corrosion rate or actual metal loss. Infrared Thermography (IRT) allows the recording of electromagnetic waves emitted from objects by using an infrared imaging system, such as an infrared camera. IRT is an online method of Non-Destructive Testing (NDT) meaning the delays in receiving results from a laboratory experienced in many NDT techniques can be eliminated. It is non-intrusive which means no process disruption and downtime will be imposed to the production line. It is also a non-contact method which mitigates the hazard occurrence and need for highly experienced personnel. The work presented here constitutes an overview on the applications of infrared thermography for the detection and characterisation of general metal loss in metallic materials. It reviews the fundamentals and represents the advances of thermography applications specifically in metal loss/thickness variation measurement along the recent literature.

1. Introduction

1.2. Common types of corrosion

1.1. Corrosion as a safety, environmental and financial risk

The corrosion mechanisms that can be identified by means of visual inspections constitute a major group of corrosion failures. A comparison between two different chemical plants located (a) in Germany and (b) in the U.S. emphasises that the visually detectable corrosion mechanisms (i.e. general/uniform, pitting, crevice and galvanic) constitute about 40–50% of total possible corrosion failures in large chemical plants regardless of the plants location and their external/operating environment [13], see Fig. 1. As result, general corrosion detection in form of measuring the metal loss yields a dominant activity in corrosion management and risk-based inspections. However, there are three important facts which remind us detection and measurement of general corrosion is not always a straightforward task: First, the corroding surface can be hidden from sight; second, the non-uniform nature of corrosion in real life can result localised precipitated corrosion in prone-to-failure locations (known as hot-spots); and third, visual inspection is more of a qualitative method than quantitative in which the data is more descriptive.

Corrosion is considered a destructive phenomenon that affects almost all metals. It is defined as the degradation of metallic materials through deterioration of their surface and internal micro-structure as a result of reaction with corrosive environments [12,13]. There are reported incidents where corrosion failure has caused catastrophic financial and environmental impacts and yet loss of life. Table 1, represents a summary of some recent catastrophic incidents as a result of corrosion failure. There is $2 trillion estimated cost of corrosion management [19] and $7 billion is estimated for the cost of manual corrosion inspection across all industries. This includes $4 billion cost of preparation i.e., scaffolding, insulation removal, etc. [20]. Oil and gas industry solely acquires 81.9% share of the global corrosion monitoring market [21]. Refineries around the world are suffering from the annual expenditure of $6 billion addressing corrosion issues including $1.3 billion for global spent on annual equipment failure and downtime [20].

⁎

Corresponding author. E-mail address: [email protected] (S. Doshvarpassand).

https://doi.org/10.1016/j.infrared.2018.12.006 Received 27 September 2018; Received in revised form 12 November 2018; Accepted 3 December 2018 Available online 10 December 2018 1350-4495/ © 2018 Elsevier B.V. All rights reserved.

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Nomenclature

¯

µ ¯

¯ ¯

c d e f h k kb p t ta,tb

t t' tPCT tPST tPSDT A

FO L Q R T T0 T TAC TDAC T DAC

specific heat (J/kg K) defect diameter/lateral extension (J/kg K) energy emitted from object (W/m2) energy emitted from blackbody (W/m2) Fourier number (–) material thickness (m) absorbed energy density (J/m2) thermal contact resistance (m2 K/W) temperature (K) initial temperature (K) the temperature accumulation at long time after pulse (K) absolute temperature contrast (–) differential absolute contrast (–) corrected differential absolute contrast (–)

Tdef Tfin Tinf Tplate Ts inf [A] [S ] [U ] [ ]

temperature of defective body (K) temperature of finite-thickness body (K) temperature of the infinite-thickness body (K) temperature of planar body of thickness L (K) temperature of semi-infinite body (K) input data matrix covariance matrix eigenvector matrix diagonal matrix of eigenvalues

C D E

total absorbance (–) thermal diffusivity (m2/s) emissivity (–) radiation wavelength (m) thermal diffusion length (m) total reflectance (–) density (kg/m3) complex thermal wave number (–) the Stefan-Boltzmann’s constant (W/m2 K4) total transmittance (–) phase angle (Rad) modulated frequency (Rad/s) thermal reflection coefficient (–) light speed (m/s) defect depth (m) thermal effusivity (Ws1/2/m2 K) excitation wave frequency (Hz) the Planck’s constant (Js) thermal conductivity (W/m K) the Boltzmann constant (J/K) laplace variable (–) time (s) time characteristics of temperature evolution associated with least-square curve fitting function (s) DAC time instant (s) first derivative time characteristics of logarithmic temperature (s) peak-contrast time characteristic (s) contrast peak-slope time characteristic (s) peak second-derivative time of logarithmic temperature (s) signal amplitude (m)

corr

Indices atm d i, j obj refl s tot

parameters related parameters related parameters related parameters related parameters related parameters related total parameter

to to to to to to

atmosphere defect pixel spatial domain object (greybody) the object reflection sound area

Table 1 The examples of recent reported incidents as result of corrosion failure. Year

Incident

Consequences

Cause

1988

A 19-year-old Boeing 737 lost a major portion of the upper body in full flight.

– One fatality. – 65 injured.

1992

The sewer explosion in Guadalajara, Mexico.

– – – –

2010

The Enbridge** Lakehead crude oil pipeline (from Indiana to Ontario) suffered an underground break in Michigan.

– 4 m litres loss of crude oil. – $585 m clean-up costs and were expected to rise by 20% more. – $61 m fine.

The incident investigated by NTSB* and concluded that the accident was caused by metal fatigue in lap-joints rivets accelerated by deformation due to the increased volume of the corrosion products over the original material [13,16]. The investigation began with tracing a loss of pressure in a gasoline pipeline from a nearby refinery. The loss of pressure was found the result of the perforation of the pipeline, leading to a leak of gasoline which subsequently caused the explosion. The leak was localised and it revealed the town water pipes installed five years previously, had been incorrectly installed very close to the steel hydrocarbon pipeline. This provoked the corrosion and then the perforation of the pipeline [13,17]. Ruptured pipeline as a result of sub-surface corrosion [12,18].

215 fatality. 1500 injured. 1600 damaged buildings. $75 m estimated damage costs.

* NTSB: United States National Transportation Safety Board. ** Enbridge: Canada’s largest pipeline company.

1.3. Corrosion monitoring as a challenge

Health Monitoring (SHM). The failure of infrastructure is mostly the result of ongoing degradation caused by natural force or internal processes. SHM involves the observation of a structure or mechanical systems over time. This can be done by using periodic measurement, recognition of susceptible-to-degradation equipment and statistical analysis of measurement data to estimate the current and future state of system health [22]. Identification and characterization of corrosion damage is one of the most significant activities in SHM. It consists of timely

Condition-Based Maintenance (CBM) is defined as the diagnosis of a failure or a prognosis of a component’s time to failure. Condition Monitoring (CM) techniques as the key component of CBM strategies, allow the maintenance actions to be based on the variations of trends in machines or process parameters such as heat, noise, vibration, wear as well as corrosion rate/metal loss. Condition monitoring of infrastructure however, is considered in line with the context of Structural 367

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General corrosion Intergranular corrosion Corrosion fatigue Hydrogen embrittlement Stress corrosion cracking (SCC) Pitting Wear, erosion, cavitation High temperature

Germany

Others

USA

Fig. 1. A comparison between corrosion failure mechanisms in two large chemical plants located in (a) Germany and (b) the U.S. [12,13].

periodic investigations performed by specialised personnel, using expensive technologies with a relatively timely process of receiving results or interpretations from laboratories; fairly unreliable results or interpretations of results in case of manual procedures i.e., visual inspection; a considerable amount of preparation for inspection i.e., scaffolding, insulation removal, etc.; significant risk of human injuries during inspection and lastly, causing plant shut-down when conducting some methods which in total, impose significant costs and downtime. In corrosion monitoring, the final purpose is the corrosion damage identification and predicting the asset remaining life through assessment of damages severity and mechanism. However, unlike damage identification in the context of machinery CM, the damage needs to be first localized across the structure before assessment and prediction of future health status [23].

monitoring are highly in favour of predicted future trend as they are characterised with direct, on-line, non-intrusive and almost non-contact characteristics referred by authors as four-leaf clover of condition monitoring techniques. The current work will review the application of Infrared Thermography for corrosion defect detection and characterisation in metallic materials. IRT represents many advantages over other NDT condition monitoring technologies: - IRT is a non-contact technic meaning the detector is not in contact with the object. Then extremely high temperature or dangerous objects can be monitored safely [4]. - IRT is a non-invasive and non-intrusive technique meaning it does not affect the target or disrupt its operation in any way [4,25]. - IRT provides two-dimensional measurements (images) resulting the comparison between different regions of the target and convenience in interpretation [4]. - IRT is an on-line (real-time) technique which enables large-field monitoring with immediate result (with aid of advanced image processing techniques) [4,26]. - IRT does not impose harmful radiations compare to other technologies e.g. X-ray imaging. Thus, it is suitable for long-term and repetitive use [4]. - IRT based condition monitoring techniques require minimal and fairly inexpensive instrumentations [25]. - The training hours required for IRT equipped inspectors (i.e. level I certification) is less than required training hours for other NDT techniques e.g. ultrasounds and x-rays [26].

1.4. Corrosion monitoring techniques Corrosion monitoring includes a broad spectrum of technical activities to cover corrosion measurement, control and prevention. These activities can be categorised to two distinct lines of work; first, activities those contribute to corrosion control, mitigation and prevention i.e. cathodic/anodic protection, material selection, corrosion allowance considerations, chemical (inhibitors) injection and external/internal coating. Second, the activities those assist with evaluating the effectiveness of above mentioned corrosion mitigation procedures during asset operation. The second group of activities are conducted by the measurement of process/environment corrosivity as well as corrosion rate. Corrosion inspection and measurement techniques may acquire a combination of four main characteristics illustrated in Fig. 2. Table 2, demonstrates the most common practices and techniques of corrosion monitoring comprising different characteristics. It is estimated that the global corrosion monitoring systems market will grow 10.68% by 2021 [24]. Also it is predicted that by 2025 there will be considerable adoption of non-contact methods involving nonintrusive technologies compared to intrusive devices (i.e., probes.) [21]. As shown in Table 2, non-destructive methods of corrosion

2. Background 2.1. Temperature, a measure of structural health Infrared thermography or thermal imaging is considered a non-destructive examination method which allows observing the heat patterns on an object surface [26]. Temperature is one of the most usual measures of the equipment and components health [25]. Every object at a

Corrosion Measurement Techniques Characterisitcs Direct/Indirect

On-line/Off-line

Intrusive/Non-intrusive

Contact/Non-contact

Direct measure of metal loss or corrosion rate (Quntitative) vs. infer that a corrosion environemnt may exsit (Qualitative) (Q alitative) (Qu

Real-time measurement with immediate results vs. off-line measurement with the results determined in a labarootoryy analysis analys y is

Constrantly exposed to the process stream vs. no disrupption for the plant or process

Equipment and presonnel in contact with the object vs. distant/remote monitoring and measurement

Fig. 2. Four main characteristics of corrosion measurement and inspection techniques. 368

369

Ultrasonic Testing (UT) Eddy Current/Magnetic Flux Test (MT) Acoustic Emission (AE) Infrared Thermography (IRT) Radiographic Testing (RT)

, etc.

NDT

, CEION

Microcor

Electrical Resistance

TM

Linear Polarisation Resistance (LPR) Tafel Extrapolation Cyclic Potentiodynamic/ Potentiostatic Galvanostatic/Galvanic Current/ZRA Electrochemical Noise (EN)/ Impedance Spectroscopy (EIS [30,31])

Electro-chemical Methods

TM

Operating conditions (Service type, Pressure, Temp., Flowrate, PH) Dissolved Oxygen/Carbonate and sulphide compounds concentration Concentration of metal ion or inhibitors Microbiological analysis Weight loss (Corrosion) coupon Thermogravimetric Analysis (TGA) Quartz Crystal Microbalance (QCM)

Analytical Chemistry

Weight Loss/ Balance

Subsets

Technique

In general, measurement of wall thickness variations through send and receive of energy or wave altered by discontinuities.

Measurement of changes (increase) in electrical resistance associated with changes (reduction) in probe cross-section due to corrosion (metal loss)

Measurement of either electric potential (indication of metal tendency to corrode) or current (indication of corr. rate) variations in an accelerated (polarised) corroding electrode compare to a freely corroding reference electrode with either similar (LPR) or dissimilar (Galvanic current, ZRA) material.

Measurement of metal loss/gain using respectively measurement of weight loss or weight imbalance

Measurement and registration of parameters indicating the presence of corrosive environment

Mechanism

Table 2 A summary of most practiced corrosion monitoring and measurement techniques.

Direct

On-line

On-line

On-line

Direct

Direct

Offline

Offline

On-line

Online/ Offline

Direct

Indirect

Direct/ Indirect

Characteristics

Non-intrusive

Intrusive

Intrusive

Intrusive

Intrusive

Intrusive/ Non-intrusive

Contact Non-contact Contact

Contact Contact

Contact (For installation/ maintenance/ removal)

Contact (For installation/ maintenance/ removal)

Contact (For installation/ maintenance/ removal)

Contact

Both

Contact/Non-contact

- Measurement of actual metal loss - Sensitivity to low corr. rate (0.1 mm/y) [28,29]; - Instantaneous [13,28,29]; - Measure of actual corr. rate instead of average rate [28]; - Comparing relative corr. rate for different process variables [28]; - Relatively useful for identification of pit initiation [29] - Measurement of actual metal loss - Suitable for all environment (except liquid metals, etc.) [28]; - Permanent installation until probe end of life [28]; - Almost Instantaneous [28,29]; - Continues and real-time measurement [28,29]; - Sensitivity to low corr. rate [28]. - Measurement of actual metal loss - Part of maintainers’ daily job; - No process disruption or shutdown; - Almost Instantaneous; - Real-time measurement.

- Measurement of actual metal loss; - Suitable for all environment; - Information about the type of corrosion can also be provided [28];

- A routine practice in corrosion assessment activities (a portion of operator’s daily activities) [27]; - Low cost of sampling and analysis.

Major Advantages

- Some methods require postprocessing for quantitative measurement; - Some methods are prone to unreliability caused by field environment.

- Low sensitivity to rapid corr. change (unless using thin probes with short life) [29]; - Relatively unreliable at probe end of life due to non-uniform nature of corrosion [28,29]; - Relatively unreliable for the presence of scales [29].

- Only the average rate of uniform corrosion can be measured [28]; - Susceptible to error for localised high corr. rate [28]; - Plant/service shut-down; - A period of exposure is required. - No measurement of localised corr. rate [29]; - Not applicable in nonconductive environments [28,29]; - Prone to measurement error caused by solution resistance, electrical charges, etc. [29]; - Plant/service shut-down for maintenance/installation.

- Qualitative; - Only the presence of corrosive environment can be confirmed; - Most result interpretation requires laboratory waiting time; - Some samplings require plant/ service shut-down.

Major Limitations

S. Doshvarpassand et al.

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temperature T above absolute zero (i.e., T > 0 K) radiates electromagnetic radiation. From wavelength perspective, this radiation falls into the infrared segment of the electromagnetic spectrum (wavelength in the range of 0.75–1000 μm) [6], see Fig. 3. Then this radiation is visually traceable by infrared detectors. Gustav Kirchhoff (1860) introduced the concept of blackbody as an ideal surface which neither reflects ( ¯ , total reflectance = 0) nor transmits ( ¯ , total transmittance = 0), but absorbs all incident radiation ( ¯ , total absorbance = 1) [6]. While a blackbody is a perfect absorber of incident radiation, it is also a perfect radiating body:

inside the object while heating or cooling. In contrast, passive thermography does not require external source of stimulation as the object is at naturally different temperature than background e.g. human body. Also the object can propagate electromagnetic heat wave due to the abnormal loading i.e. fatigue, impact or tensile loading. Nevertheless, passive thermography is considered more of a qualitative method which results identification of abnormal temperature patterns. In contrast, the controlled condition of active thermography experiment including the amount and form of stimulation often allows not only identification of defect but also a quantitative analysis of anomalies e.g. characterisation of defect physical and thermal properties. Fig. 5 demonstrates a comparison between different methods and applications of IRT. It reveals that regardless of the industry type, wherever the inspection method associates with the context of NDE/ DPA (meaning an off-line non-periodic inspection to characterize and evaluate damages/damage properties in a controlled laboratory environment) an active method of thermography has most likely been used. In the contrary, wherever the application of SHM/CM (periodic inspections of equipment taking place in the field environment) is of interest, passive technique is most likely exploited. This observation is also consistent with Usamentiaga et al., 2014 point of view on diversity of thermographic techniques and applications [4]. Along the recent decades, some industries e.g. civil industry has benefitted from both active [290,291] and passive [292,293] methods of defect detection through thermal inspection. Sub-surface corrosion defect detection however, has been mostly applied in form of NDT/MDE. In very few works, has been tried to tap into the in-situ application of corrosion/ metal loss defect detection.

(1)

= ¯=1

where , Emissivity, is the measure of a surface capability to emit energy. Joseph Stefan in 1879 (experimentally) and Ludwig Boltzmann in 1884 (theoretically) found that the amount of , the energy emitted from a blackbody surface is proportional to T 4 , the fourth power of its absolute temperature [6]. By integrating the Planck’s law (1900) over the entire wavelength spectrum, the total radiation intensity Known as StefanBoltzmann’s law is obtained:

Eb =

2 hc 2 0

5 (e hc / kb T

1)

d = ¯T 4

(2)

where ¯ as the Stefan-Boltzmann’s constant ( ¯ = 5.67 × 10 W/m K4, h is the Planck’s constant (h = 6.6 × 10 34 Js), c is the speed of light (c = 3 × 10 8 m/s), kb is the Boltzmann constant (kb = 1.38 × 10 23 J/K), is the wavelength of radiation and T is the absolute temperature of blackbody [6]. A real object however, almost never follows this law; although it may approach the behaviour of a blackbody in certain spectral domains. The final result of an infrared imaging system is the image of surface energy intensity map, see Fig. 4. An extensive literature review is performed in order to identify the current status of thermography applications in corrosion monitoring. Published works along the recent five years (2013 to present) including the key-words “Thermography” and “Thermal imaging” have been reviewed. Four most popular scientific directory have been selected to conduct the search: Science Direct, Springer, IEEE and ASME. Articles characterised with the subjects of condition monitoring in health and medical [32–34], Food and agriculture [35–39] and Wood industries [40–42] are excluded from this review. Table 3 summarises the most common applications of thermography related to non-destructive examination and condition monitoring. SHM is considered as identifying damage in structures in a global and on-line basis using sensors in a periodic inspection manner. As result, SHM and CM are analogous to each other [22]. Considering the fine line of difference between NDE (inspection activity in a local and off-line manner with priority of often characterizing priory known damages) and SHM/ CM, we classified in Table 3, the applications of thermographic techniques to two major categories of SHM/CM and NDE/DPA. 8

2

2.3. Excitation mechanisms in active thermography Active thermography is considered the most practiced approach for non-destructive inspection of surface and sub-surface defects in conductive materials. Thermal camera records both the temporal and spatial evolution of surface temperature from the moment of stimulation till stabilising to ambient state. The capability of a real object to emit the absorbed electromagnetic radiation is limited by its surface emissivity, ε < 1 [4,6,294]. The precise adjustment and calibration of emissivity during experiment will result accurate measurement of radiated electromagnetic energy which is known as radiometry. As result, experiments concerning with radiometric adjustments, delivers not only images characterised with visually traceable energy intensity variation but also the accurate interpretation of radiometric values to temperature values for each point in the field of view [294]. In a real-life non-adiabatic situation, the emissivity is not the only influential parameter affecting the accuracy of thermography inspection. As shown in Fig. 6, for a real object, the total amount of electromagnetic energy, Etot received by a thermal camera is measured based on Stefan-Boltzmann’s law and it consists of [4,6]:

2.2. Active and passive thermography

Etot = Eobj + Erefl + Eatm

From experimental approach point of view, thermographic techniques are classified into two major categories: Active and Passive. In active techniques, additional to thermal camera, an external excitation source is required for the purpose of stimulating the thermal evolution

where the Eobj corresponds to the energy emitted by the object, Erefl is the reflected energy from the surroundings emitted by the object and Eatm is the energy emitted from the atmosphere. A real object is generally considered as a Greybody. A greybody is an object whose

Fig. 3. Electromagnetic spectrum [5,6]. 370

(3)

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(b)

(a)

Fig. 4. Examples of infrared thermography for structural health and condition monitoring and the comparison between (a) visual images and (b) thermal images [4–6].

emissivity is independent of wavelength range in which the thermal camera performs. However, as the average of emissivity on the wavelength range in which thermal camera works is normally assumed, a real object is treated as a greybody. This assumption allows the object 0 . Also reflectance, ¯obj = 1 obj as the greybody absorbance, ¯ grey the atmosphere emittance, atm = 1 ¯arm as the reflectivity of atmosphere, ¯obj 0 . Then from Eq. (3):

Tobj =

4

Etot

(1

obj )· ¯atm· ¯ ·(Trefl )

4

obj· ¯atm· ¯

(1

Magnetic current stimulation can result the heat increase near the surface. As result, we will be able to not only register the magnetic current deviation due to near surface anomalies but also record the surface thermal intensity variations as the result of internal current density variation recorded by thermal cameras. Vibrothermography [301–303] is the method of thermography using the intergranular excitation of material in the vicinity of a defect. This results the micro-frictions and irreversible conversion of mechanical energy into heat along discontinuities. Such excitation can be either externally induced mechanical excitation with relatively low frequencies (20–50 Hz) or short (50–200 ms) high frequency (15–40 kHz) ultrasonic/sonic pulses or bursts. Then the temperature difference at the surface can be recorded by a thermal camera. Finding small and closed defects can be one of the major advantages of this method however it is not always practical due to accumulation of different inspection bias. Microwave thermography [304,305] is a recent technique of thermography which is considered a variation of vibrothermography. Microwaves are a form of electromagnetic radiation with wavelengths ranging from one meter to one millimetre; with immensely high frequencies between 300 MHz and 300 GHz. This results heated object due to an electromagnetic phenomenon known as dielectric loss of medium. In general, materials reflect different dielectric characteristics. A perfect dielectric material is a zero electrical conductor compare to perfect conductors with minimum dialectical characteristics. Dielectric loss simply is the loss of electromagnetic energy propagating inside a dielectric material. This loss of energy is proportional to heat variation. Consequently, microwave excitation results loss of energy or heat deviation in material which can be reordered by thermal cameras. A very few reports of using cooling mechanisms instead of heating have been mentioned in the body of literature. Considering that the heat transfer mechanisms for heating and cooling are similar, it will be worthwhile further investigation of cooling methods practicality in filed inspection application. As a matter of fact, not only the corrosion occurrence accelerates in high temperature but also the distribution of plants susceptible to corrosion i.e. oil and gas plants across hot and dry geolocations can be a motivation for the further investigation. Hinted by Maldague [306], another advantage of a cold thermal source is that it does not induce superior thermal reflections into the IR camera as it does in the case of a hot thermal source.

¯atm)· ¯ ·(Tatm ) 4 (4)

Calculating object temperature from Eq. (4) considering the reduction of surroundings and atmosphere effects is called compensation [4]. The compensation of obj and Trefl is of most interest and importance in thermographic inspection calibration and adjustment. The different methods of such calibrations have been adequately addressed in ASTM E1862-14 [295], ASTM E1933-14 [296] and ISO 18434-1:2012 [297]. Depending on the mechanism of stimulation, active thermography can be classified into different methods. Table 4 illustrates a comparison between the most practiced means of active thermography for defect (metal loss/wall-thinning/cracks defect) detection in the recent literature: Optical Thermography, is benefited by using optical stimulation devices i.e. photographic (Xenon) flashes and Halogen lamps. The fundamental differences between excitation mechanisms of these two devices determine two important methods of active thermography respectively known as Pulsed and Lock-in thermography which will be explained in details in latter section. Using photographic flash, we are able to submit a high energy heat pulse in a short (few ms) time interval. In contrast, halogen lamps provide lower amount of energy submitted to the object in relatively longer time frame (few seconds) in either continuous or modulated form. Laser thermography [298,299] is a variation of optical excited thermography. However, the application is more suitable for detection of defects perpendicularly broadened on surface (i.e. cracks). As result, more surface defects are detectable although it does not comprise a common practice for corrosion defect detection. Induction thermography [300] or generally known as eddy current stimulation thermography comprises the same idea of using eddy current application in magnetic flux (MT) non-destructive testing. 371

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Table 3 A summary of the most common applications of thermography in industrial SHM, CM and NDE (2013 to present) [43–289].

SHM: Structural Health Monitoring, CM: Condition Monitoring. NDE: Non-Destructive Examination, DPA: Destructive Physical Analysis, Cum.: Cumulative.

3. Experimental methodologies for corrosion defect detection

Solving the heat flow equation for a certain boundary condition results estimating useful information such as temperature values over the time. The classical model of heat conduction addressed by Carslaw and Jaeger [327] known as Fourier equation is as follows:

3.1. Heat diffusion through defective solids The physical foundation of defect detection using thermal methods is based on the phenomena of heat conduction (diffusion) in solids. 372

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Corrosion/Crack detection

Composite

NDE/DPA

Civil

Civil

Weld inspection Composite

Fatigue damage characterisation

Electronics

Fatigue damage characterisation

Electronics

Civil, Composite, Power

Weld inspection

Machinery CM

SHM/CM

Solar & Wind

Leakage detection

Active IRT Passive IRT

Civil

Machinery CM

Power

Solar

Fig. 5. A comparison between different methods and applications of IRT in the literature (2013 to present).

T = t

2T

T = t

(5)

2T

(6)

z2

where z corresponds to the coordinate parallel to material thickness. The solution of Eq. (6) for a Dirac delta pulse plane source of strength Q/ C launched from the surface (z = 0) of a semi-infinite medium (z ≫ 0) into that medium is as follows [327]:

2

where = k / C is thermal diffusivity (m /s). k is thermal conductivity (W/mK); is density (kg/m3) and C is specific heat (J/kg K). Two important assumptions here are adiabatic conditions resulting neglecting the heat loss and the material being isotropic and opaque. In reality however, inability to satisfy the boundary conditions, has led to difficulties in some of the classical techniques. Parker et al. [1] addressed two critical deficiencies in meeting the boundary conditions resulting difficulties in solving classical heat equation. These two deficiencies include surface heat losses and thermal contact resistance between the specimen and its associated heat sources. The flash (pulse) excitation is proposed to eliminate the effect of contact resistance between heat source and surface. The effect of surface heat loss was reduced by measurement of heat front in infinitesimally short time of pulse propagation (milliseconds) [1]. Considering the surface is uniformly heated, thermal propagation into the body of the sample may be treated as a one-dimensional heat flow process [1,3,327]. 1D heat flow then is governed by simplifying the Eq. (5) as follows:

Ts

inf

Q

(z, t ) = T0 +

where Ts

inf

e

t

z2 Q = T0 + |z = 0 4 t e t

exp

corresponds to the temperature evolution in the semi-in-

finite body, T0 is the initial (ambient) temperature, z = 0|z = 0 is considered as the boundary condition stating no heat generation at the kC is the thermal effusivity (Ws1/2/m2 K). surface and e = Obtaining the temperature domain history at the surface of an object using thermographic camera is considered a key analytical feature. In fact, additional to analytical and numerical solutions, registering of temperature patterns at the surface of objects constitutes visualisation of temperature domain history. From the right-hand side of Eq. (7), the surface temperature of semi-infinite homogenous and opaque medium decays with the slope of 1/ t . Any changes to this characteristic results intensity variations through thermal image reflecting regions T

Heat Source Absorption 0

Transmission

(1

Reflected .

.

.(

.(

(7)

)4

Atmosphere

)4

Emitted

1

).

.

.

.

)4

.(

+ .

+ .(

.(

)4

)4

Specimen (Greybody)

Fig. 6. Greybody surface behaviour against incident radiation and the effect of surroundings. 373

Camera

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Table 4 A summary of most practiced excitation methods in active IRT for detection of corrosion/crack defects in the recent literature (2013 to present) [307–326,250,271,276,277,253,254,263,267,268,279,255,269,273,258,261,262,264,274].

containing sub-surface defects. The presence of sub-surface defect will disrupt (reduce) the diffusion rate of heat front inside the object. Consequently, the defects will appear in the image with different intensity compare to the sound area, see Fig. 7. The approximation of materials as layered finite planar bodies constitutes an important consideration in defect detection process. Motivated by Carslaw and Jaeger’s [327] solution for temperature distribution in uniform thickness, L, Parker et al. [1] developed a relation for the surface temperature history of finite uniform thickness plate submitted to a Dirac pulse which is as follows:

Tplate (z , t )|z = 0 = T0 +

Q 1+2 exp( n2 2FO ) CL n= 1

camera and excitation source located at the front surface of the specimen. Such configuration is known as reflection configuration compare to transmission configuration in which the heat source is located at the opposite side of the camera behind the specimen front surface. The ability of providing quantitative information about defects was first addressed in bonded structures [328]. A key step was simulating the defects as the delamination between a 2-layer material [2]. However a comprehensive study of interface defect properties and characteristics has been a turning point for simulating the corrosion defect [329]. Through the literature, corrosion defect is generally simplified by a semi-infinite air gap located beneath the surface of a single layered solid by a distance L and in a plane parallel to surface. As result, the measurement of corrosion is reduced to measurement of the local wall thickness, L [14,329]. The air gap defect is characterised with much less thermal conductivity than the solid and it will reflect the majority of the incident thermal energy, coming from the pulse excitation, back towards the surface [3]. The thermal reflection coefficient, , of an interface defect is equivalent to [329,330]:

(8)

where FO = t / L2 is known as Fourier number. One important information from Eq. (8) is that at a certain time, the heat front reaches at the back surface and accumulates to a final constant temperature value equivalent to Q/ CL . The term inside the brackets represent the resultant heat reflections inside the material. Considering the fact that in case of inspecting structures for hidden corrosion there is normally no access to inspect from the corroded side, the majority of research associated with metal loss defect detection are characterised with both

=

em ed em + ed

(9)

where em and ed are respectively the thermal effusivity of solid material 374

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Heat response received by camera for a defective area (at a time instant, t)

A

L

Defective area (a blind flat-bottom hole)

d

Heat excitation source

Non-defective area (Sound)

Thermal camera

y Heat response received by camera for a sound areaa (at a time instant, t)

z

z

A

Section A-A

Fig. 7. Schematic of heat diffusion and response through a defective solid.

kg K) submitted to a Q = 10 (kJ/m2) Dirac delta pulse.

and defect. In case of corrosion defects modelled as air gap defect, = 1 is a valid approximation considering em ≫ ed . Another representation of temperature excursion through a finite body was proposed by some authors [3,331] as follows:

Tfin (z , t )|z = 0 = T0 +

Q e

t

nexp

1+2 n=1

n2

L2 t

3.2. Excitation waveform In addition to excitation mechanisms used for the purpose of defect detection, the excitation wave form is also of great interest. In fact, controlling the magnitude, frequency and submission time of the incident heat or cold wave is highly influential in conducting an effective active thermography experiment. It is however important to stress, the necessity of using a specific waveform of excitation is highly dependent to the nature of defect and type of experiment. Either of pulse (waveform’s given stimulation time) or modulated (waveform’s given stimulation frequency) form of energy wave corresponds to respectively two main groups of thermography techniques: Transient and Stationary. In techniques characterised by transient state the data acquisition is performed while specimen heating or cooling is in transition mode before it reaches to steady state. In contrast, stationary techniques are characterised by a modulated heating or cooling waveform for certain frequencies while the energy submission process reaches to a permanent state. In this work, the most practiced excitation waveforms

(10)

Again, the summation appearing in the bracket accounts for the effective multiple internal reflections, or reverberations, of the incident pulse between the air gap defect and the sample surface [3]. A crucial assumption here is the large extend of the defect, D along lateral direction compare to its depth, d or D ≫d [332], see Fig. 8a. In real corrosion case, defect lateral extension can be finite. As result, the incident heat front traveling above the defect will deviate laterally, see Fig. 8b. The lateral heat diffusion resulting 3D heat transfer through medium has been addressed by some authors [3,333–335]. As shown in Fig. 8b, a diffusion enhancement (arrow #2) proposed over the defect. In fact, the lateral diffusion of heat incident towards defect edges will result less heat accumulation at the centre of defect and consequently less temperature record at the surface over the defect. Based on 2D heat equation, Almond and Pickering [332] proposed an analytical model of heat diffusion. This model considers a reduction of thermal incident with defect finite lateral size, D due to lateral thermal diffusion. It was assumed that the defect edge acts as a heat sink transferring the heat incident from the high temperature area above the centre of defect towards low temperature area of defect underneath to establish the steady-state thermal equilibrium (arrow #3). It was then estimated that the associated reduction of heat over the defect is proportional to defect diffusion distant, D/2 as below:

Tdef (z, t )|z = 0 = T0 +

Q e

exp

t

nexp

1+2

(D /2) 2 4 t

n=1

d2 n2 t

L

L

d d D

D>>d

1

#1

#1

#2

#3

(11)

Table 5 represents a summary of mentioned analytical and mathematical models of heat diffusion respectively for semi-infinite, planar/ finite and defective bodies. The heat equations are solved and plotted for a typical steel sample with the density of = 8400 (kg/m3), thermal conductivity of k = 40 (W/m K), specific heat coefficient of C = 460 (J/

(a)

(b)

Fig. 8. Comparison of the effect of defect finite lateral extension on heat diffusion. 375

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Table 5 Summary of classical models of heat conduction in solids [327,1,331,336,332,11,335,337].

corresponding to the three most popular methods of active thermography used for corrosion/metal loss defect detection i.e. Pulsed Thermography (PT), Lock-in Thermography (LT) and Step heating Thermography (SHT) are reviewed.

3.2.1. Pulsed thermography (PT) The idea of pulsed thermography (PT) was first proposed by Parker et al. [1]. However, other researchers [2,3,328,329,331,338–340] incorporated this method into various NDT applications. The concept in PT experiments for the purpose of defect detection, consists of material specimen submitted to a relatively short energy pulse and then record

Table 6 Summary of the energy waveforms and their equivalent temperature response for sound (P#1) and defective (P#2) points at the surface [10,14,15,305,350,6,7].

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the temperature rise, decay or both curve in transient mode (over the time). The thermal (hot or cold) front propagates under the surface by diffusion according to the Fourier diffusion equation, see Eq. (5). The presence of defects disrupts the diffusion rate of the energy pulse. Consequently, each defect will appear in different time than the other defects located at different depth [306,341]. Dirac pulse is defined as an intense unit-area pulse of so short duration that no measuring equipment is capable of differentiating it from a shorter pulse [342,343]. However, in practice, producing ideal Dirac pulse is not possible. Different waveform can be used for the purpose of pulsed thermography. Table 6 represents a summary of explained waveforms and their thermal response for a specimen with circular flat-bottom defect located in the opposite surface of view. The most practiced waveform in PT is Square pulse of width t and amplitude A. However, the pulse width can be various from an approximate ∼2 ms (Dirac pulse) to a couple of seconds (Long pulse).

heat wave propagation. However, instead of temperature decay in PT, the period of temperature increase in stationary regime is of interest to be recorded by thermal cameras. The reason behind using sinusoidal waveforms is the ability of preserving the shape and frequency while the only deviation from reference wave will be the magnitude and phase delay [342]. As result, the key feature in LT, is the time dependency between submitted (reference) temperature signal and recorded temperature signal along one complete modulation cycle, see Table 6. In fact, the lock-in term refers to the necessity to monitor the exact time dependence between the recorded temperature signal and the reference signal (i.e. the sinusoidal-modulation heating) [306]. Recording and superimposing thermal response onto reference signal in spatial domain at a same time domain results extraction of useful information about defect retrieved from amplitude and phase data. 3.2.3. Step heating thermography (SHT) Step heating thermography or occasionally referred as time-resolved infrared radiometry (TRIR) was introduced by MacLauchlan Spicer and his colleagues in order to assess coating disbands [347], composite damages caused by impact [348], etc. Step heating thermography constitutes continuous heating or cooling the specimen while the temperature evolution is observed. Unusual behaviour in the evolution curves of an area, either in heating or cooling, determines the presence of an irregularity or defect [4]. Occasionally, through the literature, step heating and long (square) pulsed thermography have been considered identical [306,349]. Nevertheless, in long pulsed thermography, heating is applied for a selected period of time and then thermal images are collected for the temperature decay section. In step heating thermography, however, thermal images are collected whilst the specimen is submitted to energy incident (heating or cooling) [7].

3.2.2. Lock-in thermography (LT) Thermal wave imaging (also known as lock-in or modulated thermography) was introduced by Busse et al. [344] and developed by some other researchers [345,346]. In lock-in technique, the specimen is submitted to a modulated (normally a sinusoidal wave with a given modulated frequency, ω and magnitude, I) waveform. The exposure time can be a minimum of one modulation cycle until the specimen surface temperature reaches to the stationary regime [306,342,346]. The 1D solution for an isotropic semi-infinite specimen submitted to a periodic thermal wave is as follows [342,345]:

T (z, t ) = T0exp

z 2 z cos µ

t

(12)

where = 2 f (rad/s) is the modulated frequency, f (Hz) is the heat wave frequency, = 2 µ (m) corresponds to thermal wavelength. µ = ( / f ) (m) is expressed as thermal diffusion length which is equivalent to the rate of decay of the thermal wave as it penetrates through the material [342]. Similar to PT, the sub-surface defects will act as a barrier against the

Table 7 Summary of the two common models of metal loss and the schematic of their temperature, temperature contrast and derivatives variations for different thickness of a steel substrate [335,360,358,336,11].

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4. Experimental and analytical methods for corrosion defect characterisation

TAC [ij] (t ) = T[ij] (t )

The index s relates to the sound area and the indices i and j corresponds to the position of any point of interest (Also known as pixel) in a thermogram. Images acquired using infrared cameras are converted into visible images by assigning a color or intensity to each infrared energy level corresponding to either electromagnetic flux or the exact temperature (through radiometry). The result is called a thermogram [4], demonstrates the use of thermal contrast to extract important information about defect depth and size. Depends on which one of the metal loss models is being considered, temperature contrast or its derivative can be used to enhance the visibility of defective areas.

4.1. Classical corrosion models Using various IRT methodologies in a qualitative manner will result only the detection of defects. However, the characterisation of corrosion/metal loss defects i.e. defect sizing, defect depth retrieval/reconstruction and defect thermal properties e.g. thermal diffusivity, is considered the IRT quantitative approach to evaluate corrosion/metal loss. Some authors [14] simplified the corrosion defect equivalent to thickness variations considering the hypothesis that corrosion phenomena does not significantly change the thermal properties of the material of interest. As result, the temperature evolution in both sound and affected areas could be described by 1D finite body relations at an early time of excitation. Vavilov et al. [14] defined the factor known as general sensitivity to material loss meaning there is an 1% temperature increase in a corroded area for each 1% loss of material at t → ∞:

L/ L T /T

1

4.2.2. Differentiated absolute contrast Absolute thermal contrast mentioned in the previous section is based on two important assumptions. First, there is sufficient knowledge about the location of a sound area within the infrared camera field of view and second, the consideration of heat stimulation as uniform over the specimen [352]. However, such knowledge about the location of non-defective area is not always available while the thermal stimulation is barely uniform especially in case of PT experiments. Pilla et al. [352], proposed a modified version of absolute thermal contrast known as differentiated absolute contrast (DAC). In DAC, it is considered entire the specimen surface acts as non-defective for a time, t (which is an instance between the time of heat pulse launch and the time of appearance of the first defect on the thermogram). Considering the thermal evolution for semi-infinite body, Eq. (7), then:

(13)

where L /L is the relative material loss and T / T is the relative temperature increase. When quantitative analysis (characterisation) of defect/metal loss is of interest while the experimental results of thermography (temperature evolution by time) are available, the Inverse Problem needs to be solved. The inverse problem refers to acquiring the knowledge on the internal structure and properties of the material, including its non-uniformities treated as defects. Defects are recognised by analysing the variation of surface temperature evolution recorded by a thermal camera [294,306,336,351]. In contrast, solving Direct Problem consists of predicting the material thermal behaviour by either obtaining the analytical solutions of heat evolution using finite element/difference or considering heat transfer models. It is important to stress, solving the inverse problem usually requires a priori knowledge about material thermal properties especially the thermal diffusivity as well as the presence and location of non-defective (reference) area. Using least square fit through inverse problem results [14]:

L /L

T /T 1 + T /T

1

Tsound/ Tdefect

(15)

Ts (t )

Ts [ij] (t ) = T[ij] (t )

t t

(16)

Considering initial temperature of T[ij] (0) and replacing Eq. (16) into Eq. (15):

TDAC [ij] (t ) = (T[ij] (t ) = T[ij] (t )

T[ij] (0))

(Ts [ij] (t )

T[ij] (0)) = T[ij] (t )

Ts [ij] (t )

t T[ij] (t ) t

(17)

DAC accuracy can be reduced as the time elapses and also as plate thickness increases for a non-semi-infinite specimen. This drawback has addressed by some authors [355]. Transforming the differential heat equation of a slab of thickness L and initial temperature of zero using Laplace transform, Benitez et al. [355] proposed the following relation which is considered the corrected form of DAC:

(14)

The Eq. (14) demonstrates that the relative material loss can be represented as the comparison of temperature increase over the defective area with a reference area. Such comparison is universally known as thermal contrast. Table 7 Represents the two common and convenient classical models of material loss i.e. two-plate and circular flat-bottom holes models simulating thickness variations adjacent to infinite air-gap. In order to characterise the defect, the comparison of defective area/current thickness with a reference area/reference thickness of known thickness is paramount. Through the literature, the reference area has been mostly described as semi-infinite body in order to take the benefit of using 1D heat diffusion analytical model. However, some authors [332,335] advised that the 1D model is only applicable while the lateral extension of defect is considered sufficiently large compare to defect depth, see Table 5.

L TDAC

corr [ij] (t )

1

pL2

coth

p t

= T[ij] (t )

L

1

coth

pL2

T[ij] (t )

p t

(18)

where L 1 is the Laplace inverse operator and p is the Laplace variable. While the corrected DAC does not depend on heat density which suppress the uneven heating stimulation, it contains the specimen thickness L by which the need of acquiring knowledge about the physical properties of the non-defective area e.g. thickness is sensible. It is however clear that in case of an extended sub-surface corrosion attack in which the material thickness inside the field of inspection might be totally modified by corrosion, consideration of uniform thickness can be an erroneous assumption. Other measures of thermal contrast also can be used which have been adequately summarised by two recent reviews [15,356].

4.2. Thermal contrast-based techniques 4.2.1. Absolute temperature contrast Among all proposed thermographic processing techniques for the purpose of defect detection, thermal contrast computations are the most practiced in order to enhance subsurface defect visibility. It also enables some quantitative extractions such as defect depth, size and thermal properties [352]. The consideration of a non-defective reference area and calculate the temperature difference for spatial domain over temporal domain can be found through some early works [3,353,354]. This difference known as absolute temperature contrast is as follows:

4.3. Temperature signal reconstruction (TSR) using temperature and contrast derivatives By far, the described method of defect characterisations using 378

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thermal contrast are simply supported by the fundamentals and physics of heat transfer in solid mediums. The major thermographic processing methods that will be momentarily explained are mathematical and analytical procedures of thermographic signal processing and do not have physical justification. Using derivatives of logarithmic temperature decay and temperature contrast functions is considered one of these methods. Introduced by Shepard et al. [357,358], thermographic signal reconstruction (TSR) method is based on the high order polynomial curve fit over the logarithmic temperature data:

which occurs before both tPCT (the time of peak contrast) and tPST (the time of peak-slope of contrast) for flat-bottom hole and two-plate model respectively. As result, it is considered a more reliable temporal characteristics to evaluate the defect accurate depth. One benefit of tPSDT , the second derivative peak position itself is considered as a reference to ensure the reliability of polynomial fit [335,359]. In case of first derivative of logarithmic temperature function, im' ' portant information relates to the presence of time values, tmin and tmax which are respectively the time of temperature first derivative reaching to its minimum and maximum values. It is recently advised [11] that such time values corresponds to the lateral diffusion of heat due to finite lateral extension of defect as they are detectable only for small defect aspect ratio, D /d . Consequently, mentioned time values are useful to measure the dimensional parameters of defects characterised with small aspect ratio when is compared with the finite thickness (twoplate) model derivatives. As shown in Table 7, important assumptions have been made in case of each model of metal loss. For instance, if the area characterised with different thicknesses are sufficiently distant along lateral extension in the two-plate model, then the 1D analytical heat diffusion is sufficiently applicable and is in good agreement with experimental results [335]. However, in case of real corrosion defect, this assumption may not be valid. On the other hand, the model accounting for the effect of lateral extension (see Eq. (11)), is benefited by the ideal assumption of solid characterised with sufficiently large (almost equivalent to semi-infinite) thickness of sound area compare to defect depth, d [332]. The material thickness range in which NDT using IRT applies however cannot be considered as semi-infinite. Such issues have been recently addressed

N

an [ln (t )]n

ln [T (t )] =

(19)

n=0

(-)

(-)

For a defect free specimen, Eq. (19) will results a straight line with slope of 1/2, see Table 5. Any diversion from this reference line will imply the presence of defect. High order polynomial fit is however susceptible to producing unsolicited noise known as Runge effect. Using derivatives of temperature contrast function or logarithmic temperature function (depending on the model of metal loss) [11,335,358,359], enhances the contrast by introducing an early peak time. When the metal loss defect is modelled as two adjacent plates with different thicknesses, the plot of temperature contrast, TAC over time, t does not provide sufficient information about defect appearance time with reference to a sound area, see Table 7. First derivative of temperature contrast function, T' AC , however contains a time characteristic when the magnitude of T' AC reaches to maximum. This time is known as the peak-slope time of contrast, tPST [335,360]. As shown in Table 7, there is another time characteristics known as tPSDT (the peak-second derivative time of logarithmic temperature)

(-)

(-)

(-)

(-)

(-)

(-)

Fig. 9. The FEM simulation of normalised temperature (a), normalised temperature contrast (b) and normalised logarithmic derivatives (c, d) of a point over the centre of a flat-bottom defect characterised with constant D and d (D = 12 mm and d = 2 mm) and different values of sound area thickness of steel plates performed by Oswald-tranta [11] (Figure is reproduced with permission from [11]). 379

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[7,11], comparing temperature evolution between defect and sound area for thin specimens. Oswald-Tranta [11] has addressed the issue of finite sound thickness and performed FEM analysis for flat-bottom holes of constant d and D located in various finite thickness material, see Fig. 9. The results show, the thinner the sound thickness, the earlier the temperature signature above defect will be equalised with the temperature of finite thickness specimen. As result the maximum contrast time, tPCT , can occur earlier for small thickness aspect ratio, s = LS / L, see Fig. 9b. In fact, it is shown that the importance of using thermal contrast and temperature derivative methods is about finding an early time of defect appearance as less as affected by the 3D thermal diffusion complexity.

independent. Higher Order Statistics (HOS) has been used in order to enhance the conventional thermal contrast and defect detectability [364–366]. This method uses 3rd order (skewness), 4th order (Kurtosis) or 5th order statistical moments to compare the frequency (scores) of defect and sound contrast distribution. By this one can demonstrate the increase of efficiency in thermographic technique, reduction of data set volume to one single image and less susceptibility to non-uniform heating or shape of object surface. 4.5. Matrix factorisation techniques Principal Component Analysis (PCA) for thermography known as PCT, is based on the singular value decomposition (SVD) of measured temperature signature. This technique reduces the dimensionality of thermal data from three dimensions (spatial and temporal) to two dimensions [367]. The method reconstructs the input data (images) in a matrix in which each row consists of a raster-like arrangement of each single image pixels. Consequently, each column represents the temporal evolution of a certain pixel. Then using eigenvalues and eigenvectors as the essential products of SVD, the input data (reconstructed matrix) will be decomposed to its empirical orthogonal functions (EOF) [367–369]. Considering matrix A (M × N) as the rearranged input data then:

4.4. Statistical techniques Some pre-processing and post-processing techniques are benefited by statistical basis. These methods are only mathematical representatives of data with no consideration of physical underlying models and boundary conditions: Absolute peak-slope time (APST) uses creating an inflection point on temperature first derivative function by multiplying temperature decay curve by time powered by 0.5 [361]. This method is based on recognising a characteristic time to correlate with defect depth. Some authors [362], optimised the power of time factor with 0.4. The final purpose in this method is reference-independent processing technique similar to derivatives techniques. However this method is relatively prone to noise similar to derivative methods. Least-Square Fitting (LSF) directly uses the temperature decay curve to determine the characteristic time by least-square fitting method over the temperature data points [363]. Unlike, derivative and APST methods, LSF is not susceptible to noise while it is also reference

S = (A

Amean )(A

Amean )T = U U T

(20)

where S is the covariance matrix; U corresponds to the matrix containing the eigenvectors of S = (A Amean )(A Amean )T . Also is a diagonal matrix containing the singular values (non-negative square root) of ST = (A Amean )T (A Amean ) eigenvalues [368]. Usually the images that are the product of two largest eigenvalues, represent the important information about spatial field and defect respectively, see

Fig. 10. An example of thermogram sequences with the resolution of 640 × 512 and the acquisition frequency of 30 fps lasting for 2 s. Raster-like arranged input matrix is shown. 380

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Fig. 10. The data compression and dimensionality reduction aspect of PCT has been found convenient to produce the feed for intelligent image processing techniques e.g. Neural Network [314,315]. Some researchers [370] compared PCA and ICA (Independent Component Analysis) to characterise impact damages in composite. The ICA mechanism is similar to PCA (both reflect the greatest variance in dataset) however the transformed data in ICA are not empirically orthogonal but are independent of each other. In fact, in PCA only some of components (for example first and second ones) are important while in ICA due to independency of component they are all equally important and representative of a feature e.g. various anomalies, material layers and etc.

(P1,1, P2,1,

, P640,1) (P1,2, P2,2,

,

P640,2) (P1,1, P2,1, A=

, P640,1) (P1,2, P2,2,

, P640,1) (P1,2, P2,2, P640,2)

,

N 1

P640,512)@t1 ,

P640,2) (P1,1, P2,1,

(P1,512, P2,512,

the resolution of defects are highly influenced by thermal wave frequency; meaning the deeper the defect, the lower frequency is required [344]. As shown in Table 6, using four data points on a complete modulation cycle, the phase and amplitude values for each pixel can be calculated. Consequently amplitude image are used to quantify defect size and shape and phase image are used to evaluate the defect depth [259]. Increasing data points will result reduction in noise. Pulse phase thermography (PPT), introduced by Maldague and Marinetti [341]. It combines the advantages of PT and LT. This technique uses the unscrambling feature of Discrete Fourier Transform (DFT) in order to decompose the thermal signal to its amplitude and phase modules. A pulse thermal response (thermal decay curve) can be transformed from time domain to frequency domain as follows:

(P1,512, P2,512,

Fn = t ,

(P1,512, P2,512, P640,512)@t60

i2 kn ) = Ren + iImn N

(21)

where T (k t ) denotes the temperature at location p in the kth image of the sequence; Re and Im correspond respectively to real and imaginary parts of transformation; n corresponds to the frequency increment (n = 0, 1, …N); Δt known as the sampling (acquisition) rate is the time interval between images as result the range of frequency, f, can be selected from the span between 0 and 1/Δt. Eventually, the amplitude and phase delay modules will be calculated as below:

P640,512)@t2 ,

T (k t ) exp ( k=0

, 60 × 327680

4.6. Phase sensitive techniques

An =

Phase sensitive modulated wave technique known as Lock-in thermography (LT) has been addressed in previous sections, see Table 6. Exposing the specimen to modulated thermal wave in stationary regime will result appearance of subsurface anomalies. However, the depth and

Ren2+ Imn2 ;

n

= tan

1

Imn Ren

(22)

Data acquisition interval plays a critical role in effectiveness of PPT. In order to cover the entire range of defect depths in the test, all the

Table 8 The summary of temperature signal/thermal image processing techniques used for characterisation of hidden air-gap defects (e.g. thickness variation, flat-bottom holes, etc.) in the recent literature (2013 to present) [321,374–396,251,252,261,308,11,313,116,307,253,362,310,322,311,128,131,260,317,128,317,316, 145,248,318,325,315,314].

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necessary frequencies need to be covered. Aside from such drawback, PPT has the advantage of supressing the effect of non-uniform heating and emissivity variations. Also reconstructing phase image will obviate the need of priori knowledge of a non-defective area as the reference.

T = t

2T

T (x , z, t ) = T0 |t = 0 , Initialcondition T

= Q (x , t )|z = 1 and

T

= 0|z = S (x ) , Boundaryconditions (23)

where Q (x , t ) corresponds to heat flux (periodic [8]) function, T (x , z , t ) corresponds to temperature response function, denotes outward normal derivative on the boundary condition and S (x ) is the function representing the physical boundary condition of back surface. Considering isolated (air-adjacent) back wall and uniform heating accompanied by knowing S (x ) , will result solving the forward problem. However, when S (x ) is unknown, it can be found from knowing Q (x , t ) and the heat response behaviour at the front surface which requires solving inverse problem. Every pixel at the top surface needs to be analysed for the time transient regime to result the estimation ofS (x ) . It has been addressed however the effectiveness of such mathematical model can be compromised by the point selection process at the front surface and lack of priori knowledge of back surface spatial characteristics, S (x ) . This mathematical solution of inverse problem has been exploited by other authors to quantify the hidden corrosion for partially accessible back surfaces [400]. Some authors [9] developed a solution for the case of general hidden corrosion based on experimental results of PT inspection. Authors advised on two critical limitations of PT and their influences on reconstructing the back surface physical features. First, the depth dependency of defect detectability and resolution in PT which is the result of time transient lateral heat diffusion. It has been discussed that many processing techniques for thermographic data are based on the thermal contrast at the early time of detecting the defects. The reason is first, avoiding the effect of lateral heat diffusion by considering the thermal evolution in 1D space and second, the consideration of defects in regular (flat-bottom) shapes and sufficiently distant from each other. The second limitation in PT proposes the defect reconstruction as an illposed problem. Well-posedness or ill-posedness are the third characterising condition in finding a solution for an inverse problem besides existence and uniqueness. From physical point of view, ill-posedness means reconstructing back surface is highly dependent to defining the initial data. In contrast, a well-posed inverse problem means small changes in initial data results small changes in reconstruction solution [400]. Real corrosion not only appears dissimilar to flat-bottom holes but also a real corrosion defect needs to be usually considered as a single complex geometrical boundary. It has been advised that neglecting the lateral heat flow in solving inverse problem of 1D solutions can cause significant errors if defect size is small compare to its depth (which is a common case in pitting corrosion) [351]. A proposed iterative algorithm performs the defect shape correction by comparing the back surface measurement (from analytical 1D models) with simulation (FEM) result of heat diffusion at an early time [9]. The basis for the comparison process is set upon the assumption that the heat pulse launched from surface of specimen is fully reflected from the finite thickness, d of the specimen and received at the surface (meaning the pulse travelled 2d from surface till returning back to surface). Such assumption incurs a differential temperature increment represented as Tincr compare to a semi-infinite body. This temperature difference contains the combined effects of 1D finite body and lateral diffusion. As

4.7. Artificial intelligence based techniques In recent years, the impact of human vision on interpreting the image data has been drastically reduced by increasing the presence of machine vision and artificially intelligent (AI) image processing techniques. Machine Learning (ML) is a branch of AI involved with automating analytical algorithms which deals with multivariate and multiparametric problems. Machine learning is to help computers to learn and adapt without implementing explicitly high volume manual programming [371]. An early work by Saintey and Almond [372], can be considered as the literature first attempt to exploit the advantage of Artificial Neural Network (ANN) for the purpose of detection and characterising the sub-surface defects. The objective of such methods is to compute a function that measures the error (difference) between output (e.g. defect or not, defect shape and size classes, defect depth ranges, etc.) and a desired score (e.g. trained/supervised experimental dataset of known defects features such as thermal contrast, phase contrast and etc.) [373]. Another type of learning algorithms that can be used for the purpose of defect detection and characterisation is Clustering Algorithms known as Kohonen. Presence of defect in a sound background represents two different subset of data with different thermal intensities. As result, clustering algorithms are able to differentiate various visual intensities in an image. Clustering algorithms are based on allocating each pixel to a random subsets (cluster) of data in which the distance between that specific pixel and subset centre is minimum compare to other subsets [373]. Table 8 represents a summary of most practiced post-processing techniques in order for characterising the subsurface air-gap defects. Regardless of inspected material type, air-gap defect detection by surface thermal evolution follows a similar physical fundamental of heat diffusion in solids. As result, Table 8 showcase also a comparison between defect detection in metallic components and composite structures. It is important to note, works associated to intermediate anomalies e.g. disbanding and delamination are excluded from this statistical infographic. 5. Corrosion defect reconstruction Based on the most practiced RPs and standards for asset integrity and fitness for service e.g API-579-1, measuring the metal loss across equipment is paramount. These RPs usually recommend two methods of measuring metal loss: The point thickness reading (PTR) measurement and the critical thickness profiling (CTP). In case of the first assessment level, uniform thickness is mostly considered by averaging the thickness readings across the equipment body. Such estimation may cause overestimation or underestimation of corrosion severity across the equipment. In more rigorous assessment levels, expensive and sophisticated methods e.g. ultrasonic (UT) or radiography (RT) have been advised to provide an accurate picture of the hidden surfaces [397]. Hidden surface reconstruction has recently been at the centre of attention in the NDT field. Extracting the general form of corrosion requires solving the Fourier heat diffusion equation, Eq. (5), for an inverse problem. Bryan and Caudill [8,398,399] have developed significant mathematical solution for such problem. They proposed the consideration of direct (forward) problem with an additional boundary condition as a representative of back wall geometrical properties, see Fig. 11.

=1

Solid

Air-gap defect

Fig. 11. Schematic of air-gap defect represented as a mathematical function [8]. 382

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emission, the prohibition of applying non-intrinsically safe electrical equipment will result the increase of cost of equipment and energy sources. This could be a motivation for exploring other form of excitation techniques e.g. cooling, etc. Corrosion defects are generally modelled as the simplified cases of flat-bottom holes or thickness variation accompanied by simplifying assumptions and priori knowledge of defect properties when using IRT techniques. However the complexity of both the corrosion defect in its real forms and the physics of heat diffusion when dealing with complex corrosion geometries can reduce the accuracy of analytical models. As result, in recent years, the attempt for reconstructing the hidden surface affected by the corrosion incident rather than only extracting defect depth and size has been the centre of attention. It is noted that the majority of research conducted in order to characterise or even reconstruct the defects are still depend on the consideration of a nondefective reference area. The importance of acquiring knowledge about the back surface in real corrosion incident is not only about the characterisation of metal loss but also is about specifying metal gain/scale as the product of corrosion phenomena can be various [257]. There is a great potential for future research works on characterising and reconstructing corroded/gained surface independent of a reference area. It is revealed not all the common and popular methods of thermal image processing are perfectly capable of providing accurate heat signature mapping. In fact, the Region of Interest (ROI) in reconstruction activity can be as extended as the entire field of view (not only the pixels over random defects). As result, the processing technique will be involved with pixel by pixel operations which normally result the complications in processing method, computing speed and the thermal data storage. In recent years, methods of processing benefited by AI features have assisted researchers to resolve the above-mentioned issues. However, very few works have been registered in this review accounting for exploiting the use of high performance methods of machine learning and deep learning in order to reconstruct the hidden corroded surface. There is a great potential of using AI associated methods in IRT based defect characterisation and reconstruction to overcome the complexity and volume of data that needs to be processed.

=

Fig. 12. The model of temperature wave propagation in solid with thickness d and the assumption of fully reflected heat wave proposed in [9] (Figure is reproduced with permission from NDT and E International and [9]).

result, the thickness at any point over the surface of specimen can be estimated according to the value of Tincr at that point, see Fig. 12. It is important to stress, the thermal properties and the geometry of the specimen are considered known in order to provide an initial condition to commence the iterative process of shape reconstruction. Mentioned algorithm is simplified based on 1D diffusion model and the sufficient lateral extension of defects, D. A great potential exists for implementing iterative reconstruction algorithms considering the analytical models accounting for defect finite lateral extension effect [332] or finite non-defective reference thickness [11]. 6. Conclusion Current overview recalled the criticality of the hidden corrosion presence across industries and discussed the importance of measurement, mitigation and control of corrosion phenomena. It was shown that the appearance of corrosion incident in a general form of metal loss constitutes a major portion of corrosion failures in a predominant industry like oil and gas. From application perspective, an ideal condition monitoring technique was noted to be characterised with the four characteristics of Direct, On-line, No-intrusive and Non-contact. IRT demonstrates highly capable to be implemented as an ideal condition monitoring technique. It is revealed that defect detection and characterisation including cracks and corrosion in metallic component comprises the second most attended application of thermography in recent literature. The majority of research performed in order for detecting and characterising sub-surface metal loss defects are conducted using active IRT in a NDE (off-line, non-periodic inspection to characterize and evaluate damages/damage properties of priori known damages location in a controlled laboratory environment) style. Study revealed that Pulsed and Lock-in thermography using optical excitation in form of heating constitute the majority of research preformed in order for detecting and characterisation of sub-surface defects. Such popularity of PT and LT is resulted by the ease of use, inspection of wide area and the confirmed availability of various methods of processing the temperature signals/thermal images. It is however understood that these methods are highly dependent on simplifying initial and boundary conditions by which the use of IRT will be restricted to its NDE form of application. IRT techniques turn out to be highly vulnerable and compromised in case of being used in an in-situ manner. This can be the result of both object thermal properties variation e.g. emissivity, reflectivity variation and application and environmental conditions e.g. non-uniform heating, ambient temperature variation, wind and humidity presence, etc. The other obstacle against applying the IRT techniques in the field is the cost and availability of energy in order to provide excitation source. For example, in some hazardous industries e.g. LNG plants with the high rate of explosive gas

Conflict of interest The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This work as part of a research project is supported by the funding from Australian Government through Australian Government Research Training Program (RTP) Scholarship. References [1] W. Parker, et al., Flash method of determining thermal diffusivity, heat capacity, and thermal conductivity, J. Appl. Phys. 32 (9) (1961) 1679–1684. [2] D.L. Balageas, J.-C. Krapez, P. Cielo, Pulsed photothermal modeling of layered materials, J. Appl. Phys. 59 (2) (1986) 348–357. [3] S. Lau, D. Almond, J. Milne, A quantitative analysis of pulsed video thermography, NDT & E Int. 24 (4) (1991) 195–202. [4] R. Usamentiaga, et al., Infrared thermography for temperature measurement and non-destructive testing, Sensors 14 (7) (2014) 12305–12348. [5] FLIR, Thermal imaging guidebook for industrial applications, 2017. [6] C. Meola, Infrared Thermography Recent Advances and Future Trends, Bentham Science Publishers, 2012. [7] D.P. Almond, S.L. Angioni, S.G. Pickering, Long pulse excitation thermographic non-destructive evaluation, NDT & E Int. 87 (2017) 7–14. [8] K. Bryan, J. Caudill, F. Lester, An inverse problem in thermal imaging, SIAM J. Appl. Math. 56 (3) (1996) 715–735. [9] S. Lugin, U. Netzelmann, A defect shape reconstruction algorithm for pulsed thermography, NDT & E Int. 40 (3) (2007) 220–228. [10] V. Vavilov, X. Maldague, Optimization of heating protocol in thermal NDT, short and long heating pulses: a discussion, J. Res. Nondestruct. Eval. 6 (1) (1994) 1–18.

383

Infrared Physics and Technology 96 (2019) 366–389

S. Doshvarpassand et al. [11] B. Oswald-Tranta, Comparative study of thermal contrast and contrast in thermal signal derivatives in pulse thermography, NDT & E Int. 91 (2017) 36–46. [12] P.R. Roberge, Handbook of Corrosion Engineering, McGraw-Hill, 2000. [13] P.R. Roberge, Corrosion Engineering, McGraw-Hill, New York, NY, USA, 2008. [14] V. Vavilov, et al., Surface transient temperature inversion for hidden corrosion characterisation: theory and applications, Int. J. Heat Mass Transf. 39 (2) (1996) 355–371. [15] V.P. Vavilov, Pulsed thermal NDT of materials: back to the basics, Nondestruct. Testing Eval. 22 (2–3) (2007) 177–197. [16] N.T.S. Board, Aircraft Accident Report, Aloha Airlines Flight 243, Boeing 737-100, N73711, 1988. [17] Ministry in charge of the environment, D.S.B., Explosion of hydrocarbons in an urban sewerage network, 2007. [18] C. Jamasmie, Enbridge to pay $61 million fine for 2010 oil spills in Michigan, Illinois, 2016. [19] Accenture, Digital Corrosion Management: The Enormous Cost Saving Potential, 2017. < https://www.youtube.com/watch?v=1_cXhE7MzEo > . [20] G. Murphy, GE Digital Transformation Journey, Global Partner Summit, 2017, < https://www.ge.com/digital/partners/global-summit-competing-win > . [21] F. Sullivan, Analysis of the Global Corrosion Monitoring Market, Frost & Sullivan, 2016. < http://www.frost.com/sublib/display-report.do?id=MC2B-01-0000-00 > . [22] C.R. Farrar, K. Worden, An introduction to structural health monitoring, New Trends in Vibration Based Structural Health Monitoring, Springer, 2010, pp. 1–17. [23] K. Worden, et al., Structural Health Monitoring: from Structures to Systems-ofSystems★★ The support of the UK Engineering and Physical Sciences Research Council (EPSRC) through grant reference numbers EP/J016942/1 and EP/ K003836/2, and that of the EU Framework 7 Programme for the ITN project SYSWIND, is gratefully acknowledged, IFAC-PapersOnLine 48 (21) (2015) 1–17. [24] Technavio, Top 3 Trends Impacting the Global Corrosion Monitoring System Market Through 2021, Businesswire, 2017. < https://www.businesswire.com/ news/home/20170531005921/en/Top-3-Trends-Impacting-Global-CorrosionMonitoring > . [25] S. Bagavathiappan, et al., Infrared thermography for condition monitoring - a review, Infrared Phys. Technol. 60 (2013) 35–55. [26] C. Ibarra-Castanedo, J.R. Tarpani, X.P. Maldague, Nondestructive testing with thermography, Eur. J. Phys. 34 (6) (2013) S91. [27] B. Kermani, T. Chevrot, Recommended practice for corrosion management of pipelines in oil and gas production and transportation, 2012. [28] B.G. Liptak, Instrument Engineers' Handbook, Volume One: Process Measurement and Analysis, CRC Press, 2003. [29] L. Yang, Techniques for Corrosion Monitoring, Elsevier, 2008. [30] M.E. Orazem, B. Tribollet, Electrochemical Impedance Spectroscopy Vol. 48 John Wiley & Sons, 2011. [31] X. Wang, et al., Duplex coating combining layered double hydroxide and 8-quinolinol layers on Mg alloy for corrosion protection, Electrochim. Acta 283 (2018) 1845–1857. [32] S. Bagavathiappan, et al., Correlation between plantar foot temperature and diabetic neuropathy: a case study by using an infrared thermal imaging technique, J. Diabetes Sci. Technol. 4 (6) (2010) 1386–1392. [33] I. Fernández-Cuevas, et al., Classification of factors influencing the use of infrared thermography in humans: a review, Infrared Phys. Technol. 71 (2015) 28–55. [34] F. Wang, et al., Heterodyne lock-in thermography of early demineralized in dental tissues, Infrared Phys. Technol. 87 (2017) 83–90. [35] A. Gowen, et al., Applications of thermal imaging in food quality and safety assessment, Trends Food Sci. Technol. 21 (4) (2010) 190–200. [36] C. McManus, et al., Infrared thermography in animal production: an overview, Comput. Electron. Agric. 123 (2016) 10–16. [37] C.G. Pereira, et al., Infrared thermography as a complementary tool for the evaluation of heat transfer in the freezing of fruit juice model solutions, Int. J. Therm. Sci. 120 (2017) 386–399. [38] J. Gago, et al., UAVs challenge to assess water stress for sustainable agriculture, Agric. Water Manage. 153 (2015) 9–19. [39] L. Santesteban, et al., High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard, Agric. Water Manage. 183 (2017) 49–59. [40] A. Wyckhuyse, X. Maldague, A study of wood inspection by infrared thermography, Part I: wood pole inspection by infrared thermography, J. Res. Nondestruct. Eval. 13 (1) (2001) 1–12. [41] A. Kandemir-Yucel, A. Tavukcuoglu, E. Caner-Saltik, In situ assessment of structural timber elements of a historic building by infrared thermography and ultrasonic velocity, Infrared Phys. Technol. 49 (3) (2007) 243–248. [42] G. Lopez, et al., Determination of the emissivity of wood for inspection by infrared thermography, J. Nondestr. Eval. 32 (2) (2013) 172–176. [43] B. Lehmann, et al., Effects of individual climatic parameters on the infrared thermography of buildings, Appl. Energy 110 (2013) 29–43. [44] A. Moropoulou, et al., Non-destructive techniques as a tool for the protection of built cultural heritage, Constr. Build. Mater. 48 (2013) 1222–1239. [45] D. Paoletti, et al., Preventive thermographic diagnosis of historical buildings for consolidation, J. Cult. Heritage 14 (2) (2013) 116–121. [46] F. Bisegna, et al., A qualitative method for combining thermal imprints to emerging weak points of ancient wall structures by passive infrared thermography–a case study, J. Cult. Heritage 15 (2) (2014) 199–202. [47] E. Edis, I. Flores-Colen, J. De Brito, Building thermography: detection of delamination of adhered ceramic claddings using the passive approach, J. Nondestr. Eval. 34 (1) (2015) 268.

[48] M. Fox, et al., Time-lapse thermography for building defect detection, Energy Build. 92 (2015) 95–106. [49] A. Menezes, M.G. Gomes, I. Flores-Colen, In-situ assessment of physical performance and degradation analysis of rendering walls, Constr. Build. Mater. 75 (2015) 283–292. [50] E. Bauer, et al., Facades inspection with infrared thermography: cracks evaluation, J. Build. Pathol. Rehabilitation 1 (1) (2016) 2. [51] C. De Capua, R. Morello, I. Jablonski, Active and eddy current pulsed thermography to detect surface crack and defect in historical and archaeological discoveries, Measurement (2017). [52] C. Ibarra-Castanedo, et al., Solar loading thermography: time-lapsed thermographic survey and advanced thermographic signal processing for the inspection of civil engineering and cultural heritage structures, Infrared Phys. Technol. 82 (2017) 56–74. [53] A. Mohan, S. Poobal, Crack detection using image processing: a critical review and analysis, Alexandria Eng. J. (2017). [54] Y. Ham, M. Golparvar-Fard, 3D Visualization of thermal resistance and condensation problems using infrared thermography for building energy diagnostics, Visualization Eng. 2 (1) (2014) 12. [55] G. Ferrarini, et al., Thermal response measurement of building insulating materials by infrared thermography, Energy Build. 133 (2016) 559–564. [56] I. Nardi, et al., U-value assessment by infrared thermography: a comparison of different calculation methods in a Guarded Hot Box, Energy Build. 122 (2016) 211–221. [57] B.M. Marino, N. Muñoz, L.P. Thomas, Estimation of the surface thermal resistances and heat loss by conduction using thermography, Appl. Therm. Eng. 114 (2017) 1213–1221. [58] M. O’Grady, A.A. Lechowska, A.M. Harte, Infrared thermography technique as an in-situ method of assessing heat loss through thermal bridging, Energy Build. 135 (2017) 20–32. [59] B. Tejedor, et al., Quantitative internal infrared thermography for determining insitu thermal behaviour of façades, Energy Build. 151 (2017) 187–197. [60] S. Lagüela, et al., Automatic thermographic and RGB texture of as-built BIM for energy rehabilitation purposes, Autom. Constr. 31 (2013) 230–240. [61] S. Vidas, P. Moghadam, HeatWave: a handheld 3D thermography system for energy auditing, Energy Build. 66 (2013) 445–460. [62] F. Asdrubali, et al., Detection of thermal bridges from thermographic images by means of image processing approximation algorithms, Appl. Math. Comput. 317 (2018) 160–171. [63] M. Fox, et al., Thermography methodologies for detecting energy related building defects, Renew. Sustain. Energy Rev. 40 (2014) 296–310. [64] E. Edis, I. Flores-Colen, J. de Brito, Passive thermographic detection of moisture problems in façades with adhered ceramic cladding, Constr. Build. Mater. 51 (2014) 187–197. [65] E. Barreira, R.M. Almeida, J. Delgado, Infrared thermography for assessing moisture related phenomena in building components, Constr. Build. Mater. 110 (2016) 251–269. [66] M.H. Manekiya, P. Arulmozhivarman, Leakage detection and estimation using IR thermography, International Conference on Communication and Signal Processing (ICCSP), IEEE, 2016. [67] E. Barreira, R.M. Almeida, M. Moreira, An infrared thermography passive approach to assess the effect of leakage points in buildings, Energy Build. 140 (2017) 224–235. [68] Y.H. Jo, C.H. Lee, Quantitative modeling of blistering zones by active thermography for deterioration evaluation of stone monuments, J. Cult. Heritage 15 (6) (2014) 621–627. [69] S.A. Keo, et al., Steel detection in reinforced concrete wall by microwave infrared thermography, NDT & E Int. 62 (2014) 172–177. [70] J. Tashan, R. Al-Mahaidi, Detection of cracks in concrete strengthened with CFRP systems using infra-red thermography, Compos. B Eng. 64 (2014) 116–125. [71] P. Theodorakeas, et al., Quantitative analysis of plastered mosaics by means of active infrared thermography, Constr. Build. Mater. 73 (2014) 417–425. [72] P. Cotič, et al., Determination of the applicability and limits of void and delamination detection in concrete structures using infrared thermography, NDT & E Int. 74 (2015) 87–93. [73] F. Khan, et al., Modeling and experimental implementation of infrared thermography on concrete masonry structures, Infrared Phys. Technol. 69 (2015) 228–237. [74] W.W.-L. Lai, K.-K. Lee, C.-S. Poon, Validation of size estimation of debonds in external wall’s composite finishes via passive Infrared thermography and a gradient algorithm, Constr. Build. Mater. 87 (2015) 113–124. [75] E. Bauer, et al., Analysis of building facade defects using infrared thermography: laboratory studies, J. Build. Eng. 6 (2016) 93–104. [76] M.J. Matovu, A. Farhidzadeh, S. Salamone, Damage assessment of steel-plate concrete composite walls by using infrared thermography: a preliminary study, J. Civ. Struct. Health Monit. 6 (2) (2016) 303–313. [77] P. Aversa, et al., Infrared thermography for the investigation of dynamic thermal behaviour of opaque building elements: comparison between empty and filled with hemp fibres prototype walls, Energy Build. 152 (2017) 264–272. [78] K. Maroy, et al., Assessing the thermal performance of insulating glass units with infrared thermography: potential and limitations, Energy Build. 138 (2017) 175–192. [79] C. Serra, A. Tadeu, N. Simões, Heat transfer modeling using analytical solutions for infrared thermography applications in multilayered buildings systems, Int. J. Heat Mass Transf. 115 (2017) 471–478. [80] P. Cotič, et al., Image fusion for improved detection of near-surface defects in

384

Infrared Physics and Technology 96 (2019) 366–389

S. Doshvarpassand et al.

[81] [82] [83] [84] [85] [86] [87] [88] [89] [90] [91] [92] [93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] [104] [105] [106] [107] [108] [109] [110] [111] [112]

NDT-CE using unsupervised clustering methods, J. Nondestr. Eval. 33 (3) (2014) 384–397. S.K.U. Rehman, et al., Nondestructive test methods for concrete bridges: a review, Constr. Build. Mater. 107 (2016) 58–86. T. Sakagami, et al., Nondestructive evaluation of fatigue cracks in steel bridges based on thermoelastic stress measurement, Proc. Struct. Integrity 2 (2016) 2132–2139. S.A. Dabous, et al., Concrete bridge deck condition assessment using IR Thermography and Ground Penetrating Radar technologies, Autom. Constr. (2017). M. Janků, I. Březina, J. Grošek, Use of infrared thermography to detect defects on concrete bridges, Proc. Eng. 190 (2017) 62–69. T. Sakagami, et al., Verification of the repair effect for fatigue cracks in members of steel bridges based on thermoelastic stress measurement, Eng. Fract. Mech. (2017). A.A. Sultan, G. Washer, A pixel-by-pixel reliability analysis of infrared thermography (IRT) for the detection of subsurface delamination, NDT & E Int. 92 (2017) 177–186. S. Hiasa, R. Birgul, F.N. Catbas, Infrared thermography for civil structural assessment: demonstrations with laboratory and field studies, J. Civ. Struct. Health Monit. 6 (3) (2016) 619–636. S. Hiasa, R. Birgul, F.N. Catbas, A data processing methodology for infrared thermography images of concrete bridges, Comput. Struct. 190 (2017) 205–218. S. Hiasa, R. Birgul, F.N. Catbas, Effect of defect size on subsurface defect detectability and defect depth estimation for concrete structures by infrared thermography, J. Nondestr. Eval. 36 (3) (2017) 57. S. Hiasa, R. Birgul, F.N. Catbas, Investigation of effective utilization of infrared thermography (IRT) through advanced finite element modeling, Constr. Build. Mater. 150 (2017) 295–309. X. Sun, et al., Thermography analyses of rock fracture due to excavation and overloading for tunnel in 30° inclined strata, Sci. China Technol. Sci. (2017) 1–13. C. Plati, P. Georgiou, A. Loizos, Use of infrared thermography for assessing HMA paving and compaction, Transport. Res. Part C: Emerg. Technol. 46 (2014) 192–208. M. Solla, et al., Approach to identify cracking in asphalt pavement using GPR and infrared thermographic methods: preliminary findings, NDT & E Int. 62 (2014) 55–65. E. Carreño-Alvarado, et al., Identification of buried pipes using thermal images and data mining, Proc. Eng. 89 (2014) 1445–1451. G. Baldinelli, et al., Spaceborne detection of roof and impervious surface albedo: potentialities and comparison with airborne thermography measurements, Sol. Energy 113 (2015) 281–294. A. Berg, J. Ahlberg, M. Felsberg, Enhanced analysis of thermographic images for monitoring of district heat pipe networks, Pattern Recogn. Lett. 83 (2016) 215–223. A. Berg, J. Ahlberg, Classification of leakage detections acquired by airborne thermography of district heating networks. in: 2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), IEEE, 2014. O. Friman, et al., Methods for large-scale monitoring of district heating systems using airborne thermography, IEEE Trans. Geosci. Remote Sens. 52 (8) (2014) 5175–5182. P. Ala-aho, et al., Fully integrated surface–subsurface flow modelling of groundwater–lake interaction in an esker aquifer: model verification with stable isotopes and airborne thermal imaging, J. Hydrol. 522 (2015) 391–406. S. Yahyanejad, B. Rinner, A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs, ISPRS J. Photogramm. Remote Sens. 104 (2015) 189–202. E.J. Lee, et al., Early sinkhole detection using a drone-based thermal camera and image processing, Infrared Phys. Technol. 78 (2016) 223–232. A. Nishar, et al., Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): a case study of the Wairakei-Tauhara geothermal field, Taupo, New Zealand, Renew. Energy 86 (2016) 1256–1264. G. Tanda et al., Monitoring of biogas emissions from an urban landfill by means of close-range aerial infrared thermography, 2016. D. Peng, R. Jones, Lock-in thermographic inspection of squats on rail steel head, Infrared Phys. Technol. 57 (2013) 89–95. D. Peng, R. Jones, Modelling of the lock-in thermography process through finite element method for estimating the rail squat defects, Eng. Fail. Anal. 28 (2013) 275–288. A. Elhassnaoui et al., Modeling by finite element method of detecting rail squat by pulsed thermography, in: Information and Communication Technologies Innovation and Application (ICTIA), IEEE, 2014. Y. Gao, et al., Multiple cracks detection and visualization using magnetic flux leakage and eddy current pulsed thermography, Sens. Actuators, A 234 (2015) 269–281. J. Peng, et al., Investigation into eddy current pulsed thermography for rolling contact fatigue detection and characterization, NDT & E Int. 74 (2015) 72–80. C. Tang, et al., Infrared and visible images registration with adaptable local-global feature integration for rail inspection, Infrared Phys. Technol. 87 (2017) 31–39. A. Bendada, et al., How to reveal subsurface defects in Kevlar® composite materials after an impact loading using infrared vision and optical NDT techniques? Eng. Fract. Mech. 108 (2013) 195–208. L. Cheng, G.Y. Tian, Transient thermal behavior of eddy-current pulsed thermography for nondestructive evaluation of composites, IEEE Trans. Instrum. Meas. 62 (5) (2013) 1215–1222. L. Junyan, L. Liqiang, W. Yang, Experimental study on active infrared

[113] [114] [115] [116] [117]

[118] [119] [120] [121]

[122] [123] [124] [125] [126] [127] [128]

[129] [130] [131] [132] [133] [134] [135] [136] [137] [138] [139] [140] [141] [142] [143] [144] [145]

385

thermography as a NDI tool for carbon–carbon composites, Compos. B Eng. 45 (1) (2013) 138–147. R. Mulaveesala, V. Ghali, V. Arora, Applications of non-stationary thermal wave imaging methods for characterisation of fibre-reinforced plastic materials, Electron. Lett. 49 (2) (2013) 118–119. R. Usamentiaga, et al., A quantitative comparison of stimulation and post-processing thermographic inspection methods applied to aeronautical carbon fibre reinforced polymer, Quant. InfraRed Thermogr. J. 10 (1) (2013) 55–73. B. Yang, Y. Huang, L. Cheng, Defect detection and evaluation of ultrasonic infrared thermography for aerospace CFRP composites, Infrared Phys. Technol. 60 (2013) 166–173. B. Chiwu, et al., Inspection on CFRP sheet with subsurface defects using pulsed thermographic technique, Infrared Phys. Technol. 65 (2014) 117–121. A. Foudazi, K.M. Donnell, M.T. Ghasr, Application of active microwave thermography to delamination detection, in: Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International, 2014, IEEE. S. Grammatikos, et al., Current injection phase thermography for low-velocity impact damage identification in composite laminates, Mater. Des. 55 (2014) 429–441. C. Maierhofer, et al., Validation of flash thermography using computed tomography for characterizing inhomogeneities and defects in CFRP structures, Compos. B Eng. 64 (2014) 175–186. V.P. Vavilov, S. Billard, V. Ayvazyan, A thermal tomographic unit for testing composite materials, Russ. J. Nondestr. Test. 50 (11) (2014) 679–683. G. Dua, G.V. Subbarao, R. Mulaveesala, Testing and evaluation of glass fiber reinforced polymers by thermal wave imaging, in: International Conference on Signal Processing And Communication Engineering Systems (SPACES), 2015, IEEE. V. Ghali, B. Suresh, A. Hemanth, Data fusion for enhanced defect detectability in non-stationary thermal wave imaging, IEEE Sens. J. 15 (12) (2015) 6761–6762. M.R. Jolly, et al., Review of non-destructive testing (NDT) techniques and their applicability to thick walled composites, Proc. CIRP 38 (2015) 129–136. S.A. Keo, et al., Defect detection in CFRP by infrared thermography with CO2 laser excitation compared to conventional lock-in infrared thermography, Compos. B Eng. 69 (2015) 1–5. J. Liu, et al., Three-dimensional visualization of subsurface defect using lock-in thermography, Int. J. Thermophys. 36 (5–6) (2015) 1226–1235. C. Meola, et al., Nondestructive evaluation of carbon fibre reinforced composites with infrared thermography and ultrasonics, Compos. Struct. 134 (2015) 845–853. A. Foudazi, et al., Evaluation of steel fiber distribution in cement-based mortars using active microwave thermography, Mater. Struct. 49 (12) (2016) 5051–5065. H. Halloua, et al., Nondestructive inverse approach for determining thermal and geometrical properties of internal defects in CFRP composites by lock-in thermography, International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), IEEE, 2016. L. Maio, et al., Ultrasonic and IR thermographic detection of a defect in a multilayered composite plate, Proc. Eng. 167 (2016) 71–79. P.D. Pastuszak, Characterization of defects in curved composite structures using active infrared thermography, Proc. Eng. 157 (2016) 325–332. D. Roy, S. Tuli, Efficient SNR determination algorithm for lock-in thermogaphy defect detection and quantification, in: 1st India International Conference on Information Processing (IICIP), IEEE, 2016. Y. Abdulrahman, et al., A Taguchi design of experiment approach to pulse and lock in thermography, applied to CFRP composites, J. Nondestr. Eval. 36 (4) (2017) 72. A. Chrysafi, N. Athanasopoulos, N. Siakavellas, Damage detection on composite materials with active thermography and digital image processing, Int. J. Therm. Sci. 116 (2017) 242–253. F. Khodayar, et al., Optimization of the inspection of large composite materials using robotized line scan thermography, J. Nondestr. Eval. 36 (2) (2017) 32. V. Tříska, O. Flášar, Non-destructive inspection of composite specimen with integrated lightning protection using pulsed thermography, in: International Conference on Military Technologies (ICMT), IEEE, 2017. M. Zaiß, et al., Use of thermography and ultrasound for the quality control of SMC lightweight material reinforced by carbon fiber tapes, Proc. CIRP 62 (2017) 33–38. V. Zorin, N. Baurova, E. Kosenko, Detection of defects in components made of dispersion-filled polymeric materials by the method of infrared thermography, Polym. Sci., Ser. D 10 (3) (2017) 241–243. R. Marani, et al., Modeling and classification of defects in CFRP laminates by thermal non-destructive testing, Compos. B Eng. 135 (2018) 129–141. J. Peeters, et al., Optimised dynamic line scan thermographic detection of CFRP inserts using FE updating and POD analysis, NDT & E Int. 93 (2018) 141–149. G. Ptaszek, et al., Transient thermography testing of unpainted thermal barrier coating (TBC) systems, NDT & E Int. 59 (2013) 48–56. B. Ghiassi, et al., FRP-to-masonry bond durability assessment with infrared thermography method, J. Nondestr. Eval. 33 (3) (2014) 427–437. M. Lizaranzu, et al., Non-destructive testing of composite materials by means of active thermography-based tools, Infrared Phys. Technol. 71 (2015) 113–120. X. Wu, K. Peters, Non-destructive inspection of adhesively bonded joints using amplitude modulated thermography, Exp. Mech. 55 (8) (2015) 1485–1501. L.A. Abreu, et al., Thermography detection of contact failures in double layered materials using the reciprocity functional approach, Appl. Therm. Eng. 100 (2016) 1173–1178. P. Daryabor, M. Safizadeh, Investigation of defect characteristics and heat transfer in step heating thermography of metal plates repaired with composite patches, Infrared Phys. Technol. 76 (2016) 608–620.

Infrared Physics and Technology 96 (2019) 366–389

S. Doshvarpassand et al. [146] M. Barus, et al., Bonded repair issues for composites: an investigation approach based on infrared thermography, NDT & E Int. 85 (2017) 27–33. [147] X. Guo, N. Zhang, A phase sensitive modulated thermography of debondings in the insulator of SRMs, Polym. Test. 57 (2017) 226–234. [148] H. Zhao, et al., Application of lock-in thermography for the inspection of disbonds in titanium alloy honeycomb sandwich structure, Infrared Phys. Technol. 81 (2017) 69–78. [149] M. Barus, et al., NDT-based design of joint material for the detection of bonding defects by infrared thermography, NDT & E Int. 93 (2018) 157–163. [150] W. Ren, et al., Quantitative non-destructive evaluation method for impact damage using eddy current pulsed thermography, Compos. B Eng. 54 (2013) 169–179. [151] R. Usamentiaga, et al., Automatic detection of impact damage in carbon fiber composites using active thermography, Infrared Phys. Technol. 58 (2013) 36–46. [152] R. Usamentiaga, et al., Feature extraction and analysis for automatic characterization of impact damage in carbon fiber composites using active thermography, NDT & E Int. 54 (2013) 123–132. [153] Y. He, et al., Non-destructive testing of low-energy impact in CFRP laminates and interior defects in honeycomb sandwich using scanning pulsed eddy current, Compos. B Eng. 59 (2014) 196–203. [154] C. Meola, G.M. Carlomagno, Infrared thermography to evaluate impact damage in glass/epoxy with manufacturing defects, Int. J. Impact Eng. 67 (2014) 1–11. [155] H. Schmutzler, et al., Degradation monitoring of impact damaged carbon fibre reinforced polymers under fatigue loading with pulse phase thermography, Compos. B Eng. 59 (2014) 221–229. [156] S. Boccardi, et al., Effects of interface strength gradation on impact damage mechanisms in polypropylene/woven glass fabric composites, Compos. B Eng. 90 (2016) 179–187. [157] Y. Li, et al., Investigation on the damage evolution in the impacted composite material based on active infrared thermography, NDT & E Int. 83 (2016) 114–122. [158] T. Liang, et al., Low energy impact damage detection in CFRP using eddy current pulsed thermography, Compos. Struct. 143 (2016) 352–361. [159] C. Meola, et al., Infrared thermography to an aluminium foam sandwich structure subjected to low velocity impact tests, Proc. Eng. 167 (2016) 23–29. [160] I. Taraghi, A. Fereidoon, Non-destructive evaluation of damage modes in nanocomposite foam-core sandwich panel subjected to low-velocity impact, Compos. B Eng. 103 (2016) 51–59. [161] G.P.M. Fierro, et al., Imaging of barely visible impact damage on a complex composite stiffened panel using a nonlinear ultrasound stimulated thermography approach, J. Nondestr. Eval. 36 (4) (2017) 69. [162] C. Meola, et al., Impact damaging of composites through online monitoring and non-destructive evaluation with infrared thermography, NDT & E Int. 85 (2017) 34–42. [163] C. Colombo, et al., Thermographic stepwise assessment of impact damage in sandwich panels, Compos. Struct. 184 (2018) 279–287. [164] V. Dattoma, S. Giancane, Evaluation of energy of fatigue damage into GFRC through digital image correlation and thermography, Compos. B Eng. 47 (2013) 283–289. [165] C. Garnier, et al., Fatigue behavior of impacted composite structures, Compos. Struct. 100 (2013) 443–450. [166] C. Goidescu, et al., Damage investigation in CFRP composites using full-field measurement techniques: combination of digital image stereo-correlation, infrared thermography and X-ray tomography, Compos. B Eng. 48 (2013) 95–105. [167] E. Kordatos, et al., Rapid evaluation of the fatigue limit in composites using infrared lock-in thermography and acoustic emission, Mech. Res. Commun. 54 (2013) 14–20. [168] T. Lisle, et al., Damage analysis and fracture toughness evaluation in a thin woven composite laminate under static tension using infrared thermography, Compos. A Appl. Sci. Manuf. 53 (2013) 75–87. [169] W. Harizi, et al., Mechanical damage assessment of glass fiber-reinforced polymer composites using passive infrared thermography, Compos. B Eng. 59 (2014) 74–79. [170] J. Montesano, H. Bougherara, Z. Fawaz, Application of infrared thermography for the characterization of damage in braided carbon fiber reinforced polymer matrix composites, Compos. B Eng. 60 (2014) 137–143. [171] V. Vavilov, O. Budadin, A. Kulkov, Infrared thermographic evaluation of large composite grid parts subjected to axial loading, Polym. Test. 41 (2015) 55–62. [172] V. Munoz, et al., Damage detection in CFRP by coupling acoustic emission and infrared thermography, Compos. B Eng. 85 (2016) 68–75. [173] D. Palumbo, et al., A new rapid thermographic method to assess the fatigue limit in GFRP composites, Compos. B Eng. 103 (2016) 60–67. [174] P.H. Shin, S.C. Webb, K.J. Peters, Pulsed phase thermography imaging of fatigueloaded composite adhesively bonded joints, NDT & E Int. 79 (2016) 7–16. [175] R. Tamborrino, et al., Assessment of the effect of defects on mechanical properties of adhesive bonded joints by using non destructive methods, Compos. B Eng. 91 (2016) 337–345. [176] Y.M. Elsherbini, S.V. Hoa, Fatigue threshold-stress determination in AFP laminates containing gaps using IR thermography, Compos. Sci. Technol. 146 (2017) 49–58. [177] H. Cui et al., The methods in infrared thermal imaging diagnosis technology of power equipment, in: IEEE 4th International Conference on Electronics Information and Emergency Communication (ICEIEC), IEEE, 2013. [178] A. Huda, S. Taib, Suitable features selection for monitoring thermal condition of electrical equipment using infrared thermography, Infrared Phys. Technol. 61 (2013) 184–191. [179] A. Huda, et al., A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis, ISA Trans. 53 (3) (2014) 717–724.

[180] K. Endo, et al., Thermoreflectance mapping observation of Power MOSFET under UIS avalanche breakdown condition, Microelectron. Reliab. 55 (9–10) (2015) 1628–1633. [181] Z. Hui, H. Fuzhen, An intelligent fault diagnosis method for electrical equipment using infrared images, in: 34th Chinese Control Conference (CCC), IEEE, 2015. [182] Y.-W.A. Wang et al., Equipment monitoring for temperature related failures using thermography cameras, in: IEEE Petroleum and Chemical Industry Committee Conference (PCIC), IEEE, 2015. [183] M.A. Herrera-Arellano et al., Infrared thermography-based automatic assessment of control components for electric machines, in: IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), IEEE, 2017. [184] D. Kumar, M. Ansari, Condition monitoring of electrical assets using digital IRT and AI technique, J. Electr. Syst. Inf. Technol. (2017). [185] E. Mechkov, Application of infrared thermography technique in transformers maintenance in distribution network, in: 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA), IEEE, 2017. [186] L. Chengyan, Q. Fei, B. Zhaowei, Non-destructive testing for defects in electronic package based on Infrared Thermography, in: 14th International Conference on Electronic Packaging Technology (ICEPT), IEEE, 2013. [187] X. Lu, et al., Defects inspection of the solder bumps using self reference technology in active thermography, Infrared Phys. Technol. 63 (2014) 97–102. [188] D. May et al., IR pulse thermography as failure analytical tool applied to die attach processes, in: 21st International Workshop on Thermal Investigations of ICs and Systems (THERMINIC), IEEE, 2015. [189] Z. He, et al., Detection of micro solder balls using active thermography and probabilistic neural network, Infrared Phys. Technol. 81 (2017) 236–241. [190] Z. Xu, et al., Using active thermography for defects inspection of flip chip, Microelectron. Reliab. 54 (4) (2014) 808–815. [191] Y.-K. An, et al., Line laser lock-in thermography for instantaneous imaging of cracks in semiconductor chips, Opt. Lasers Eng. 73 (2015) 128–136. [192] T.D.C. Macarios, J.R. Barbosa, Infrared thermal imaging analysis of a 1-kW variable capacity compressor frequency inverter, J. Braz. Soc. Mech. Sci. Eng. 37 (1) (2015) 275–284. [193] K. Muzaffar, et al., Fault detection of antenna arrays using infrared thermography, Infrared Phys. Technol. 71 (2015) 464–468. [194] E. Johnson, Y. Kim, IR testing of microwave circulator bonds, in: IEEE Metrology for Aerospace (MetroAeroSpace), IEEE, 2016. [195] K.O. Petrosyants et al., Hardware-software subsystem for multilevel thermal fault detection and analysis of electronic components. in: International Siberian Conference on Control and Communications (SIBCON), IEEE, 2016. [196] J. Yang, et al., Multi-spot laser lock-in thermography for real-time imaging of cracks in semiconductor chips during a manufacturing process, J. Mater. Process. Technol. 229 (2016) 94–101. [197] F. Al-Obaidy, F. Yazdani, F.A. Mohammadi, Fault detection using thermal image based on soft computing methods: comparative study, Microelectron. Reliab. 71 (2017) 56–64. [198] K. Kijkanjanapaiboon, et al., Investigation of dimensional and heat source effects in Lock-In Thermography applications in semiconductor packages, Appl. Therm. Eng. 113 (2017) 673–683. [199] N. Sharp, et al., Lithium-ion battery electrode inspection using pulse thermography, NDT & E Int. 64 (2014) 41–51. [200] S. Bazinski, X. Wang, Predicting heat generation in a lithium-ion pouch cell through thermography and the lumped capacitance model, J. Power Sources 305 (2016) 97–105. [201] P. Just, et al., A method to quantify coating thickness and porosity of electrodes for lithium-ion-batteries, Measurement 89 (2016) 312–315. [202] X. Zhou, et al., Cycle life estimation of lithium-ion polymer batteries using artificial neural network and support vector machine with time-resolved thermography, Microelectron. Reliab. 79 (2017) 48–58. [203] K. Buttery, S. Alahakoon, Improving condition monitoring & fault diagnosis strategy for HV motors in coal fired power stations, in: Australasian Universities Power Engineering Conference (AUPEC), IEEE, 2013. [204] M. Eftekhari, et al., A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging, Infrared Phys. Technol. 61 (2013) 330–336. [205] G.-M. Lim, D.-M. Bae, J.-H. Kim, Fault diagnosis of rotating machine by thermography method on support vector machine, J. Mech. Sci. Technol. 28 (8) (2014) 2947–2952. [206] A. Glowacz, Z. Glowacz, Diagnostics of stator faults of the single-phase induction motor using thermal images, MoASoS and selected classifiers, Measurement 93 (2016) 86–93. [207] Q. Khan, A.A. Khan, F. Ahmad, Condition monitoring tool for electrical equipment—thermography, in: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, 2016. [208] O. Munoz-Ornelas et al., Methodology for thermal analysis of induction motors with infrared thermography considering camera location, in: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2016. [209] J.A. Ramirez-Nunez et al., Self-adjustment methodology of a thermal camera for detecting faults in industrial machinery, in:IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2016. [210] G. Singh, T.C.A. Kumar, V. Naikan, Fault diagnosis of induction motor cooling system using infrared thermography, in: IEEE 6th International Conference on Power Systems (ICPS), IEEE, 2016. [211] G.M. dos Santos, R.R. de Aquino, M.M. Lira, Thermography and artificial intelligence in transformer fault detection, Electr. Eng. (2017) 1–9.

386

Infrared Physics and Technology 96 (2019) 366–389

S. Doshvarpassand et al. [212] A. Glowacz, Z. Glowacz, Diagnosis of the three-phase induction motor using thermal imaging, Infrared Phys. Technol. 81 (2017) 7–16. [213] D. López-Pérez, J. Antonino-Daviu, Application of infrared thermography to failure detection in industrial induction motors: case stories, IEEE Trans. Ind. Appl. 53 (3) (2017) 1901–1908. [214] P. Redón et al, Reliable methodology for online fault diagnosis in induction motors using passive infrared thermography, in: IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), IEEE, 2017. [215] E. Resendiz-Ochoa et al., Segmentation in thermography images for bearing defect analysis in induction motors, in: IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), IEEE, 2017. [216] G. Singh, V. Naikan, Infrared thermography based diagnosis of inter-turn fault and cooling system failure in three phase induction motor, Infrared Phys. Technol. 87 (2017) 134–138. [217] O. Janssens, et al., Thermal image based fault diagnosis for rotating machinery, Infrared Phys. Technol. 73 (2015) 78–87. [218] A. Mohanty, S. Fatima, Shaft misalignment detection by thermal imaging of support bearings, IFAC-PapersOnLine 48 (21) (2015) 554–559. [219] H. Kim, Y. Park, J. Buongiorno, Measurement of wetted area fraction in subcooled pool boiling of water using infrared thermography, Nucl. Eng. Des. 264 (2013) 103–110. [220] X. Courtois, et al., IR thermography diagnostics for the WEST project, Fusion Eng. Des. 89 (9–10) (2014) 2472–2476. [221] K. Kamiya, et al., Design study of a wide-angle infrared thermography and visible observation diagnostic on JT-60SA, Fusion Eng. Des. 89 (12) (2014) 3089–3094. [222] A. Semerok, et al., Active laser pyrometry and lock-in thermography for characterisation of deposited layer on TEXTOR graphite tile, Nucl. Instrum. Methods Phys. Res., Sect. A 738 (2014) 25–33. [223] C. Pei, et al., Simulation of surface cracks measurement in first walls by laser spot array thermography, Fusion Eng. Des. 109 (2016) 1237–1241. [224] Z.H. Karadeniz, D. Kumlutaş, Ö. Özer, Experimental visualization of the flow characteristics of the outflow of a split air conditioner indoor unit by meshed infrared thermography and stereo particle image velocimetry, Exp. Therm. Fluid Sci. 44 (2013) 334–344. [225] S. Vintrou, et al., Quantitative infrared investigation of local heat transfer in a circular finned tube heat exchanger assembly, Int. J. Heat Fluid Flow 44 (2013) 197–207. [226] G.A. Longo, et al., HFC32 vaporisation inside a brazed plate heat exchanger (BPHE): experimental measurements and IR thermography analysis, Int. J. Refrig. 57 (2015) 77–86. [227] B.-R. Fu, et al., Real-time monitoring of a micro reformer integrated with a microchannel heat exchanger by infrared thermography and high-speed flow images, Int. J. Hydrogen Energy 41 (41) (2016) 18610–18620. [228] O. Breitenstein, et al., Comparison of DLIT-and PL-based local solar cell efficiency analysis, Energy Proc. 38 (2013) 2–12. [229] K. Ramspeck, et al., In-line thermography for reliable hot spot detection and process control, Energy Proc. 55 (2014) 133–140. [230] B. Wiengmoon, et al., Quantifying the uniformity of solar cells using thermal imaging diagnosis, Appl. Therm. Eng. 70 (1) (2014) 350–360. [231] A. Gerber, et al., Advanced large area characterization of thin-film solar modules by electroluminescence and thermography imaging techniques, Sol. Energy Mater. Sol. Cells 135 (2015) 35–42. [232] T. Kaden, K. Lammers, H.J. Möller, Power loss prognosis from thermographic images of PID affected silicon solar modules, Sol. Energy Mater. Sol. Cells 142 (2015) 24–28. [233] J. Packa et al., Chosen diagnostic methods of photovoltaic modules, in: Diagnostic of Electrical Machines and Insulating Systems in Electrical Engineering (DEMISEE), IEEE, 2016. [234] J.A. Tsanakas, L. Ha, C. Buerhop, Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: a review of research and future challenges, Renew. Sustain. Energy Rev. 62 (2016) 695–709. [235] A. Vetter, J. Hepp, C.J. Brabec, Automatized segmentation of photovoltaic modules in IR-images with extreme noise, Infrared Phys. Technol. 76 (2016) 439–443. [236] G. Álvarez-Tey, R. Jiménez-Castañeda, J. Carpio, Analysis of the configuration and the location of thermographic equipment for the inspection in photovoltaic systems, Infrared Phys. Technol. (2017). [237] B. Du, et al., Nondestructive inspection, testing and evaluation for Si-based, thin film and multi-junction solar cells: an overview, Renew. Sustain. Energy Rev. 78 (2017) 1117–1151. [238] F. Frühauf, O. Breitenstein, DLIT-versus ILIT-based efficiency imaging of solar cells, Sol. Energy Mater. Sol. Cells 169 (2017) 195–202. [239] P. Guerriero, S. Daliento, Automatic edge identification for accurate analysis of thermographic images of solar panels, in: 6th International Conference on Clean Electrical Power (ICCEP), IEEE, 2017. [240] Z.A. Jaffery, A.K. Dubey, A. Haque, Scheme for predictive fault diagnosis in photovoltaic modules using thermal imaging, Infrared Phys. Technol. 83 (2017) 182–187. [241] A. Tiddens, et al., Induced Infrared Thermography: flow visualizations under the extreme conditions of an open volumetric receiver of a solar tower, Int. J. Heat Fluid Flow 65 (2017) 105–113. [242] S. Vergura, et al., A quantitative and computer-aided thermography-based diagnostics for PV devices—Part II: platform and results, IEEE J. Photovoltaics 7 (1) (2017) 237–243. [243] Z. Zeng, et al., Breakpoint detection of heating wire in wind blade moulds using

infrared thermography, Infrared Phys. Technol. 64 (2014) 73–78. [244] B. Gao, et al., Multidimensional tensor-based inductive thermography with multiple physical fields for offshore wind turbine gear inspection, IEEE Trans. Ind. Electron. 63 (10) (2016) 6305–6315. [245] T. Worzewski, et al., Thermographic inspection of a wind turbine rotor blade segment utilizing natural conditions as excitation source, Part I: solar excitation for detecting deep structures in GFRP, Infrared Phys. Technol. 76 (2016) 756–766. [246] R. Yang, Y. He, H. Zhang, Progress and trends in nondestructive testing and evaluation for wind turbine composite blade, Renew. Sustain. Energy Rev. 60 (2016) 1225–1250. [247] S. Hwang, Y.-K. An, H. Sohn, Continuous line laser thermography for damage imaging of rotating wind turbine blades, Proc. Eng. 188 (2017) 225–232. [248] J.W. Kim, K.W. Yun, H.C. Jung, Investigation of optimal thermal injection conditions and the capability of IR thermography for detecting wall-thinning defects in small-diameter piping components, Nucl. Eng. Des. 262 (2013) 39–51. [249] A. Manohar, F.L. di Scalea, Determination of defect depth and size using virtual heat sources in pulsed infrared thermography, Exp. Mech. 53 (4) (2013) 661–671. [250] S.P.T. Quoc, G. Cheymol, A. Semerok, Phase lock-in thermography for metal walls characterization, in: 3rd International Conference on Advancements in Nuclear Instrumentation Measurement Methods and their Applications (ANIMMA), IEEE, 2013. [251] D. Sharath, M. Menaka, B. Venkatraman, Defect characterization using pulsed thermography, J. Nondestr. Eval. 32 (2) (2013) 134–141. [252] V.P. Vavilov, et al., An apparatus for the active thermal testing of corrosion in steel cylindrical containers and test results, Russ. J. Nondestr. Test. 49 (11) (2013) 619–624. [253] Y. He, et al., An investigation into eddy current pulsed thermography for detection of corrosion blister, Corros. Sci. 78 (2014) 1–6. [254] B. Lahiri, et al., Infrared thermography based defect detection in ferromagnetic specimens using a low frequency alternating magnetic field, Infrared Phys. Technol. 64 (2014) 125–133. [255] J. Liu, et al., Study of inspection on metal sheet with subsurface defects using linear frequency modulated ultrasound excitation thermal-wave imaging (LFMUTWI), Infrared Phys. Technol. 62 (2014) 136–142. [256] J. Mehnen, L. Tinsley, R. Roy, Automated in-service damage identification, CIRP Ann.-Manuf. Technol. 63 (1) (2014) 33–36. [257] A. Saifi et al., Detection of the thickness of scale on the inner surface of water pipes by infrared thermography, in: Information and Communication Technologies Innovation and Application (ICTIA), IEEE, 2014. [258] A. Foudazi, M.T. Ghasr, K.M. Donnell, Characterization of corroded reinforced steel bars by active microwave thermography, IEEE Trans. Instrum. Meas. 64 (9) (2015) 2583–2585. [259] S. Ranjit, K. Kang, W. Kim, Investigation of lock-in infrared thermography for evaluation of subsurface defects size and depth, Int. J. Precis. Eng. Manuf. 16 (11) (2015) 2255–2264. [260] V. Arora, R. Mulaveesala, P. Bison, Effect of spectral reshaping on frequency modulated thermal wave imaging for non-destructive testing and evaluation of steel material, J. Nondestr. Eval. 35 (1) (2016) 15. [261] G. Cadelano, et al., Corrosion detection in pipelines using infrared thermography: experiments and data processing methods, J. Nondestr. Eval. 35 (3) (2016) 49. [262] M. Woźny, et al., Infrared radiation emitted due to scanning of a hot spot as a probe of hidden defects, Infrared Phys. Technol. 76 (2016) 574–579. [263] C. Xu, et al., Investigation on eddy current pulsed thermography to detect hidden cracks on corroded metal surface, NDT & E Int. 84 (2016) 27–35. [264] A. El-amiri, et al., Detection of corrosion depth in bolts by thermographic model, Proc. Struct. Integrity 5 (2017) 1065–1071. [265] G. Inglese, R. Olmi, S. Priori, A procedure for detecting hidden surface defects in a thin plate by means of active thermography, J. Nondestr. Eval. 36 (3) (2017) 61. [266] L. Tinsley, et al., A study of pulsed thermography for life assessment of thin EBPVD TBCs undergoing oxidation ageing, NDT & E Int. 92 (2017) 67–74. [267] R. Yang, et al., Through coating imaging and nondestructive visualization evaluation of early marine corrosion using electromagnetic induction thermography, Ocean Eng. 147 (2018) 277–288. [268] M. Benmoussat, et al., Automatic metal parts inspection: use of thermographic images and anomaly detection algorithms, Infrared Phys. Technol. 61 (2013) 68–80. [269] X. Guo, V. Vavilov, Crack detection in aluminum parts by using ultrasound-excited infrared thermography, Infrared Phys. Technol. 61 (2013) 149–156. [270] M. Schweda, et al., Thermographic analysis and modelling of the delamination crack growth in a thermal barrier coating on Fecralloy substrate, Surf. Coat. Technol. 217 (2013) 124–128. [271] Y. Fedala, et al., Infrared lock-in thermography crack localization on metallic surfaces for industrial diagnosis, J. Nondestr. Eval. 33 (3) (2014) 335–341. [272] R. Celorrio, et al., Advances in crack characterization by lock-in infrared thermography, Int. J. Thermophys. 36 (5–6) (2015) 1202–1207. [273] Q.-x. Min et al., Recognition of contact interface defect in metal plate based on pulsed phase thermography, in: Prognostics and System Health Management Conference (PHM), IEEE, 2015. [274] M. Rodríguez-Martín, et al., Prediction of depth model for cracks in steel using infrared thermography, Infrared Phys. Technol. 71 (2015) 492–500. [275] A. El-amiri, et al., Cracks detection in Mg-Zn (Y, Gd) magnesium alloy piston heads by pulsed thermography, in: International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), IEEE, 2016. [276] A. Loeb, C. Earls, Optimized inspection design for the thermographic characterization of sub-pixel sized through cracks, NDT & E Int. 82 (2016) 44–55. [277] N. Pech-May, et al., Fast characterization of the width of vertical cracks using

387

Infrared Physics and Technology 96 (2019) 366–389

S. Doshvarpassand et al. pulsed laser spot infrared thermography, J. Nondestr. Eval. 35 (2) (2016) 22. [278] A. Saifi et al., A new model for determining the thermal effusivity of defects by pulsed thermography, in: International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), IEEE, 2016. [279] J. Zhao, et al., Crack evaluation based on novel circle-ferrite induction thermography, IEEE Sens. J. 17 (17) (2017) 5637–5645. [280] Y.-K. An, J.M. Kim, H. Sohn, Laser lock-in thermography for detection of surfacebreaking fatigue cracks on uncoated steel structures, NDT & E Int. 65 (2014) 54–63. [281] R. De Finis, et al., Fatigue limit evaluation of various martensitic stainless steels with new robust thermographic data analysis, Int. J. Fatigue 74 (2015) 88–96. [282] L. Krstulović-Opara, et al., Comparison of infrared and 3D digital image correlation techniques applied for mechanical testing of materials, Infrared Phys. Technol. 73 (2015) 166–174. [283] N. Torabian, et al., Thermal response of DP600 dual-phase steel under ultrasonic fatigue loading, Mater. Sci. Eng. A 677 (2016) 97–105. [284] H. Park, et al., A study on detection of micro-cracks in the dissimilar metal weld through ultrasound infrared thermography, Infrared Phys. Technol. 62 (2014) 124–131. [285] T. Saravanan, et al., Non-destructive evaluation of friction stir welded joints by Xray radiography and infrared thermography, Proc. Eng. 86 (2014) 469–475. [286] J.E. Seppala, K.D. Migler, Infrared thermography of welding zones produced by polymer extrusion additive manufacturing, Addit. Manuf. 12 (2016) 71–76. [287] D. Yang, G. Wang, G. Zhang, Thermal analysis for single-pass multi-layer GMAW based additive manufacturing using infrared thermography, J. Mater. Process. Technol. 244 (2017) 215–224. [288] M.S. Jadin, K.H. Ghazali, Gas leakage detection using thermal imaging technique, in: UKSim-AMSS 16th International Conference on Computer Modelling and Simulation (UKSim), IEEE, 2014. [289] P.M. Bach, J.K. Kodikara, Reliability of infrared thermography in detecting leaks in buried water reticulation pipes, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10 (9) (2017) 4210–4224. [290] C. Meola, Infrared thermography of masonry structures, Infrared Phys. Technol. 49 (3) (2007) 228–233. [291] S. Bhalla, S. Tuli, R. Arora, Defect detection in concrete structures using thermal imaging techniques, Exp. Tech. 35 (4) (2011) 39–43. [292] E. Grinzato, et al., Monitoring of the Scrovegni Chapel by IR thermography: Giotto at infrared, Infrared Phys. Technol. 43 (3) (2002) 165–169. [293] M. Wicker, B.P. Alduse, S. Jung, Detection of hidden corrosion in metal roofing shingles utilizing infrared thermography, J. Build. Eng. 20 (2018) 201–207. [294] X.P. Maldague, Introduction to NDT by active infrared thermography, Mater. Eval. 60 (9) (2002) 1060–1073. [295] ASTM, Standard Practice for Measuring and Compensating for Reflected Temperature Using Infrared Imaging Radiometers. [296] ASTM, Standard Practice for Measuring and Compensating for Emissivity Using Infrared Imaging Radiometers. [297] ISO, Condition monitoring and diagnostics of machines – Thermography – Part 1: General procedures, in ISO 18434-1. [298] J.-C. Krapez, G. Gardette, D. Balageas, Thermal ellipsometry in steady-state and by lock-in thermography, Application to anisotropic materials characterization, OFFICE NATIONAL D ETUDES ET DE RECHERCHES AEROSPATIALES ONERAPUBLICATIONS-TP, 1996. [299] F. Cernuschi, et al., In-plane thermal diffusivity evaluation by infrared thermography, Rev. Sci. Instrum. 72 (10) (2001) 3988–3995. [300] G. Riegert, T. Zweschper, G. Busse, Lockin thermography with eddy current excitation, Quant. InfraRed Thermogr. J. 1 (1) (2004) 21–32. [301] L. Favro, et al., Infrared imaging of defects heated by a sonic pulse, Rev. Sci. Instrum. 71 (6) (2000) 2418–2421. [302] G. Busse et al., Lockin vibrothermal inspection of polymer composites in: QIRT, 1992. [303] A. Salerno et al., Thermographic inspection with ultrasonic excitation, in: Review of Progress in Quantitative Nondestructive Evaluation, Springer, 1997, pp. 345–352. [304] G. Cuccurullo, et al., IR temperature measurements in microwave heating, Infrared Phys. Technol. 43 (3–5) (2002) 145–150. [305] R. Osiander, J.W. Spicer, Time-resolved infrared radiometry with step heating. A review, Revue Générale de Thermique 37 (8) (1998) 680–692. [306] X. Maldague, Applications of infrared thermography in nondestructive evaluation, Trends Opt. Nondestruct. Testing (2000) 591–609. [307] M. Shuohan, M. Qishuang, L. Xinbo, Applications of chirp z transform and multiple modulation zoom spectrum to pulse phase thermography inspection, NDT & E Int. 54 (2013) 1–8. [308] Z. Suszyński, M. Bednarek, Application of the correlation function in the detection of thermal heterogeneity using pulsed thermography, Int. J. Thermophys. 34 (4) (2013) 680–690. [309] L.D. Tomić, J.M. Elazar, Pulse thermography experimental data processing by numerically simulating thermal processes in a sample with periodical structure of defects, NDT & E Int. 60 (2013) 132–135. [310] F. Lopez, et al., Optimization of pulsed thermography inspection by partial leastsquares regression, NDT & E Int. 66 (2014) 128–138. [311] J.-Y. Zhang, et al., A novel compression algorithm for infrared thermal image sequence based on K-means method, Infrared Phys. Technol. 64 (2014) 18–25. [312] B. Yousefi, et al., Comparative analysis on thermal non-destructive testing imagery applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Infrared Phys. Technol. 85 (2017) 163–169. [313] Y. Zhao, et al., A confidence map based damage assessment approach using pulsed

thermographic inspection, NDT & E Int. 93 (2018) 86–97. [314] S. Dudzik, Analysis of the accuracy of a neural algorithm for defect depth estimation using PCA processing from active thermography data, Infrared Phys. Technol. 56 (2013) 1–7. [315] S. Dudzik, Two-stage neural algorithm for defect detection and characterization uses an active thermography, Infrared Phys. Technol. 71 (2015) 187–197. [316] R. Qian et al., Infrared nondestructive recognition of small subsurface defects using a SVM classifier, in: IEEE International Conference on Information and Automation (ICIA), IEEE, 2017. [317] N. Rajic, C. Antolis, An investigation of noise performance in optical lock-in thermography, Infrared Phys. Technol. 87 (2017) 1–10. [318] C. Xu, et al., Improving defect visibility in square pulse thermography of metallic components using correlation analysis, Mech. Syst. Sig. Process. 103 (2018) 162–173. [319] F. Cernuschi, Can TBC porosity be estimated by non-destructive infrared techniques? A theoretical and experimental analysis, Surf. Coat. Technol. 272 (2015) 387–394. [320] S. Mezghani, et al., Evaluation of paint coating thickness variations based on pulsed Infrared thermography laser technique, Infrared Phys. Technol. 76 (2016) 393–401. [321] R. Yang, Y. He, Optically and non-optically excited thermography for composites: a review, Infrared Phys. Technol. 75 (2016) 26–50. [322] X. Guo, Y. Mao, Defect identification based on parameter estimation of histogram in ultrasonic IR thermography, Mech. Syst. Sig. Process. 58 (2015) 218–227. [323] R. Yang et al., An investigation and review into microwave thermography for NDT and SHM, in: IEEE Far East NDT New Technology & Application Forum (FENDT), IEEE, 2015. [324] V. Guillebert, et al., Development of a thermographic NDI system for service damage identification in inaccessible areas, Proc. CIRP 11 (2013) 124–129. [325] J.H. Kim, et al., On-power detection of pipe wall-thinned defects using IR thermography in NPPS, Nucl. Eng. Technol. 46 (2) (2014) 225–234. [326] M. Schweda, et al., Damage evolution of a thermal barrier coating system with 3dimensional periodic interface roughness: effects of roughness depth, substrate creep strength and pre-oxidation, Surf. Coat. Technol. 276 (2015) 368–373. [327] H.S. Carslaw, J.C. Jaeger, Conduction of Heat in Solids, second ed., Clarendon Press, Oxford, 1959, p. 1959. [328] V. Vavilov, R. Taylor, Theoretical and practical aspects of the thermal nondestructive testing of bonded structures, Research Techniques in Nondestructive Testing, Academic Press, 1982, pp. 239–279. [329] P. Patel, D. Almond, H. Reiter, Thermal-wave detection and characterisation of sub-surface defects, Appl. Phys. B 43 (1) (1987) 9–15. [330] X.P. Maldague, Nondestructive Evaluation of Materials by Infrared Thermography, Springer Science & Business Media, 2012. [331] J. Milne, W. Reynolds, The non-destructive evaluation of composites and other materials by thermal pulse video thermography, in: Thermosense VII: Thermal Infrared Sensing for Diagnostics and Control, International Society for Optics and Photonics, 1985. [332] D.P. Almond, S.G. Pickering, An analytical study of the pulsed thermography defect detection limit, J. Appl. Phys. 111 (9) (2012) 093510. [333] D.P. Almond, S. Lau, Defect sizing by transient thermography. I. An analytical treatment, J. Phys. D: Appl. Phys. 27 (5) (1994) 1063. [334] D.P. Almond, S. Lau, Edge effects and a method of defect sizing for transient thermography, Appl. Phys. Lett. 62 (25) (1993) 3369–3371. [335] J. Sun, Analysis of pulsed thermography methods for defect depth prediction, J. Heat Transf. 128 (4) (2006) 329–338. [336] X. Maldague, Theory and practice of infrared technology for nondestructive testing, 2001. [337] A. Sirikham, Y. Zhao, J. Mehnen, Determination of thermal wave reflection coefficient to better estimate defect depth using pulsed thermography, Infrared Phys. Technol. 86 (2017) 1–10. [338] D. Almond, P. Patel, H. Reiter, The potential value of photothermal imaging for the testing of plasma sprayed coatings, Le Journal de Physique Colloques 44 (C6) (1983) p. C6-491-C6-495. [339] F. Lepoutre, D. Fournier, A. Boccara, Nondestructive control of weldings using the mirage detection, J. Appl. Phys. 57 (4) (1985) 1009–1015. [340] A. Mandelis, M.M. Zver, Theory of photopyroelectric spectroscopy of solids, J. Appl. Phys. 57 (9) (1985) 4421–4430. [341] X. Maldague, S. Marinetti, Pulse phase infrared thermography, J. Appl. Phys. 79 (5) (1996) 2694–2698. [342] C. Ibarra-Castanedo et al., Active infrared thermography techniques for the nondestructive testing of materials, in: Ultrasonic and Advanced Methods for Nondestructive Testing and Material Characterization, World Scientific, 2007, pp. 325–348. [343] R.N. Bracewell, R.N. Bracewell, The Fourier Transform and Its Applications vol. 31999, McGraw-Hill, New York, 1986. [344] G. BuSSe, D. Wu, W. Karpen, Thermal wave imaging with phase sensitive modulated thermography, J. Appl. Phys. 71 (8) (1992) 3962–3965. [345] L. Favro, X. Han, Thermal wave materials characterization and thermal wave imaging, Sens. Mater. Charact. Process. Manuf., ASNT TONES 1 (1998) 399–415. [346] D. Wu, G. Busse, Lock-in thermography for nondestructive evaluation of materials, Revue générale de thermique 37 (8) (1998) 693–703. [347] J.M. Spicer, et al., Measurement of coating physical properties and detection of coating disbonds by time-resolved infrared radiometry, J. Nondestr. Eval. 8 (2) (1989) 107–120. [348] J. Maclachlan Spicer et al., Time-resolved infrared radiometry (TRIR) for characterization of impact damage in composite materials, 992.

388

Infrared Physics and Technology 96 (2019) 366–389

S. Doshvarpassand et al.

(2013). [375] A.D.R. Girón, H.L. Correa, New 3D finite difference method for thermal contrast enhancement in slabs pulsed thermography inspection, J. Nondestr. Eval. 33 (1) (2014) 62–73. [376] O. Ley, V. Godinez-Azcuaga, Line scanning thermography and its application inspecting aerospace composites, in: 5th International Symposium on NDT in Aerospace, Singapore Google Scholar, 2013. [377] C. Alvarez-Restrepo, H. Benitez-Restrepo, L. Tobon, Characterization of defects of pulsed thermography inspections by orthogonal polynomial decomposition, NDT & E Int. 91 (2017) 9–21. [378] Y. Duan, et al., Quantitative evaluation of optical lock-in and pulsed thermography for aluminum foam material, Infrared Phys. Technol. 60 (2013) 275–280. [379] M. Ishikawa, et al., Detecting deeper defects using pulse phase thermography, Infrared Phys. Technol. 57 (2013) 42–49. [380] T. Qingju, et al., Inspection on SiC coated carbon–carbon composite with subsurface defects using pulsed thermography, Infrared Phys. Technol. 60 (2013) 183–189. [381] R. Waugh, J. Dulieu-Barton, S. Quinn, Modelling and evaluation of pulsed and pulse phase thermography through application of composite and metallic case studies, NDT & E Int. 66 (2014) 52–66. [382] S.-J. Tao, et al., Determination of temperature sequence length in pulse phase thermography, Infrared Phys. Technol. 72 (2015) 210–220. [383] B. Yousefi et al., Automatic IRNDT inspection applying sparse PCA-based clustering, in: IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), IEEE, 2017. [384] Q. Tang, et al., A new signal processing algorithm of pulsed infrared thermography, Infrared Phys. Technol. 68 (2015) 173–178. [385] J. Peeters, et al., Active thermography setup updating for NDE: a comparative study of regression techniques and optimisation routines with high contrast parameter influences for thermal problems, Optimization Eng. 19 (1) (2018) 163–185. [386] K. Zheng, et al., Thermographic clustering analysis for defect detection in CFRP structures, Polym. Test. 49 (2016) 73–81. [387] Q. Tang, et al., Quantitative detection of defects based on Markov–PCA–BP algorithm using pulsed infrared thermography technology, Infrared Phys. Technol. 77 (2016) 144–148. [388] G. Pitarresi, Lock-in signal post-processing techniques in infra-red thermography for materials structural evaluation, Exp. Mech. 55 (4) (2015) 667–680. [389] G. Jinlong, et al., Inverse heat transfer approach for nondestructive estimation the size and depth of subsurface defects of CFRP composite using lock-in thermography, Infrared Phys. Technol. 71 (2015) 439–447. [390] J. Liu, et al., Study on lock-in thermography defect detectability for carbon-fiberreinforced polymer (CFRP) sheet with subsurface defects, Int. J. Thermophys. 36 (5–6) (2015) 1259–1265. [391] J. Siddiqui, et al., Infrared thermal wave imaging for nondestructive testing of fibre reinforced polymers, Exp. Mech. 55 (7) (2015) 1239–1245. [392] C. Antolis, N. Rajic, Optical lock-in thermography for structural health monitoring–a study into infrared detector performance, Proc. Eng. 188 (2017) 471–478. [393] K. Chatterjee, D. Roy, S. Tuli, A novel pulse compression algorithm for frequency modulated active thermography using band-pass filter, Infrared Phys. Technol. 82 (2017) 75–84. [394] P. Daryabor, M. Safizadeh, Comparison of three thermographic post processing methods for the assessment of a repaired aluminum plate with composite patch, Infrared Phys. Technol. 79 (2016) 58–67. [395] K. Ghadermazi, et al., Delamination detection in glass–epoxy composites using step-phase thermography (SPT), Infrared Phys. Technol. 72 (2015) 204–209. [396] H. Wang, et al., Using active thermography to inspect pin-hole defects in antireflective coating with k-mean clustering, NDT & E Int. 76 (2015) 66–72. [397] API, Fitness-For-Service, in: API 579-1/ASME FFS-1, second ed., 2007. [398] K. Bryan, L. Caudill, Reconstruction of an unknown boundary portion from Cauchy data in n dimensions, Inverse Prob. 21 (1) (2004) 239. [399] K. Bryan, L.F. Caudill Jr, Stability and reconstruction for an inverse problem for the heat equation, Inverse Prob. 14 (6) (1998) 1429. [400] M. Charnley, A. Rzeznik, Thermal Detection of Inaccessible Corrosion, 2012.

[349] V. Vavilov, T. Kauppinen, E. Grinzato, Thermal characterization of defects in building envelopes using long square pulse and slow thermal wave techniques, J. Res. Nondestruct. Eval. 9 (4) (1997) 181–200. [350] A.A. Badghaish, D.C. Fleming, Non-destructive inspection of composites using step heating thermography, J. Compos. Mater. 42 (13) (2008) 1337–1357. [351] S. Grys, W. Minkina, L. Vokorokos, Automated characterisation of subsurface defects by active IR thermographic testing–discussion of step heating duration and defect depth determination, Infrared Phys. Technol. 68 (2015) 84–91. [352] M. Pilla et al., New absolute contrast for pulsed thermography, in: Proc. QIRT, 2002. [353] P. Potet, F. Lesbre, C. Bathias, Quantitative characterization of impact damage in composite materials—a comparison between computerized vibrothermography and X-ray tomography, in: H.L.M. dos Reis (Ed.), Nondestructive Testing and Evaluation for Manufacturing and Construction, 1989, pp. 95–105. [354] X.P. Maldague, Nondestructive evaluation of materials by infrared thermography, NDT & E Int. 6 (29) (1996) 396. [355] H.D. Benítez, et al., Definition of a new thermal contrast and pulse correction for defect quantification in pulsed thermography, Infrared Phys. Technol. 51 (3) (2008) 160–167. [356] V.P. Vavilov, D.D. Burleigh, Review of pulsed thermal NDT: Physical principles, theory and data processing, NDT & E Int. 73 (2015) 28–52. [357] S.M. Shepard, Temporal noise reduction, compression and analysis of thermographic image data sequences, US, 1999. [358] S.M. Shepard, et al., Reconstruction and enhancement of active thermographic image sequences, Opt. Eng. 42 (5) (2003) 1337–1343. [359] C. Ibarra-Castanedo, et al., Infrared image processing and data analysis, Infrared Phys. Technol. 46 (1–2) (2004) 75–83. [360] H.I. Ringermacher, R.J. Archacki Jr., W.A. Veronesi, Nondestructive testing: transient depth thermography, 1998, Google Patents. [361] Z. Zeng, et al., Absolute peak slope time based thickness measurement using pulsed thermography, Infrared Phys. Technol. 55 (2–3) (2012) 200–204. [362] V.P. Vavilov, S.S. Pawar, A novel approach for one-sided thermal nondestructive testing of composites by using infrared thermography, Polym. Test. 44 (2015) 224–233. [363] J. Sun, Method for determining defect depth using thermal imaging, Google Patents, 2003. [364] F. Madruga et al., Automatic data processing based on the skewness statistic parameter for subsurface defect detection by active infrared thermography, in: Proc. QIRT, Citeseer, 2008. [365] F.J. Madruga et al., Enhanced contrast detection of subsurface defects by pulsed infrared thermography based on the fourth order statistic moment, kurtosis, in: Thermosense XXXI, International Society for Optics and Photonics, 2009. [366] F.J. Madruga, et al., Infrared thermography processing based on higher-order statistics, NDT & E Int. 43 (8) (2010) 661–666. [367] N. Rajic, Principal component thermography for flaw contrast enhancement and flaw depth characterisation in composite structures, Compos. Struct. 58 (4) (2002) 521–528. [368] K.E. Cramer, W.P. Winfree, The application of principal component analysis using fixed eigenvectors to the infrared thermographic inspection of the space shuttle thermal protection system, 2006. [369] S. Marinetti, et al., Statistical analysis of IR thermographic sequences by PCA, Infrared Phys. Technol. 46 (1–2) (2004) 85–91. [370] L. Cheng, et al., Impact damage detection and identification using eddy current pulsed thermography through integration of PCA and ICA, IEEE Sens. J. 14 (5) (2014) 1655–1663. [371] A.L. Samuel, Some studies in machine learning using the game of checkers, IBM J. Res. Dev. 3 (3) (1959) 210–229. [372] M. Saintey, D. Almond, An artificial neural network interpreter for transient thermography image data, NDT & E Int. 30 (5) (1997) 291–295. [373] S. Vallerand, X. Maldague, Defect characterization in pulsed thermography: a statistical method compared with Kohonen and Perceptron neural networks, NDT & E Int. 33 (5) (2000) 307–315. [374] D.L. Balageas, In search of early time: an original approach in the thermographic identification of thermophysical properties and defects, Adv. Opt. Technol. 2013

389