In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition

Mechanical Systems and Signal Processing 136 (2020) 106526

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In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition Joham Alvarez-Montoya ⇑, Alejandro Carvajal-Castrillón, Julián Sierra-Pérez Grupo de Investigación en Ingeniería Aeroespacial, Universidad Pontificia Bolivariana, Sede Medellín, Circular 1a 70-01, 050031 Medellín, Colombia

a r t i c l e

i n f o

Article history: Received 19 October 2018 Received in revised form 22 October 2019 Accepted 14 November 2019

Keywords: Structural health monitoring Damage detection Composite materials Aerospace structures Machine learning Remote sensing

a b s t r a c t Aiming to provide more efficient, lightweight structures, composite materials are being extensively used in aerospace vehicles. As the failure mechanisms of these materials are complex, damage detection becomes challenging, requiring advanced techniques for assessing structural integrity and maintaining aircraft safety. In this context, Structural Health Monitoring (SHM) seeks for integrating sensors into the structures in a way that Nondestructive Testing (NDT) is implemented continuously. One promising approach is to use Fiber Optic Sensors (FOS) to acquire strain signals, taking advantages of their capabilities over conventional sensors. Despite several works have developed Health and Usage Monitoring Systems (HUMS) using FOS for performing in-flight SHM in aircraft structures, automatic damage detection using the acquired signals has not been achieved in a robust way against environmental and operational variability, in all flight stages or considering different types of damages. In this work, a HUMS was developed and implemented in an Unmanned Aerial Vehicle (UAV) based on 20 Fiber Bragg Gratings (FBGs) embedded into the composite front spar of the aircraft’s wing, a miniaturized data acquisition subsystem for gathering strain signals and a wireless transmission subsystem for remote sensing. The HUMS was tested in 16 flights, six of them were carried out with the pristine structure and the remaining after inducing different artificial damages. The in-flight data were used to validate a previously developed damage detection methodology based on strain field pattern recognition, or strain mapping, which utilizes machine learning algorithms, specifically a Self-Organizing Map (SOM)-based procedure for clustering operational conditions and Principal Component Analysis (PCA) in conjunction with damage indices for final classification. The performance of the damage detection demonstrated a highest accuracy of 0.981 and a highest F 1 score of 0.978. As a main contribution, this work implements in-flight strain monitoring, remote sensing and automatic damage detection in an operating composite aircraft structure. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction The seek for advanced composite materials is one of the major current research fields in aerospace engineering since the usage of these can provide reductions in airframe weight, higher corrosion resistance and a much better fatigue behavior in ⇑ Corresponding author. E-mail address: [email protected] (J. Alvarez-Montoya). https://doi.org/10.1016/j.ymssp.2019.106526 0888-3270/Ó 2019 Elsevier Ltd. All rights reserved.

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contrast with traditional metallic materials. The implementation of composite materials in primary and secondary structures has remarkably increased during the last few decades, being an important issue in the development of more efficient, less fuel-consuming aircraft like the Boeing 787 Dreamliner and the Airbus A350 [1]. In all commercial aviation activities, high safety levels must be ensured. This requires the proper assessment of threats to the structural integrity of airframes, such as material and component damages caused by degradation and exposure to the environment and operational circumstances. In order to achieve this, it is necessary to generate a deep understanding about failure modes of these advanced materials, alongside with the development of tools to estimate the remaining life and strength of composite aerospace structures [2]. Such facts represent a matter of concern to engineers since the information available about the performance of composites over time is limited in comparison with metallic materials. Additionally, damage detection is difficult in these materials because of their complex constitution and high sensitivity to damages that may not be visible. These damages are mainly produced by impacts, causing so-called Barely Visible Damages (BVD) such as delamination, matrix cracking and broken fibers [1]. As the failure mechanisms of these materials are heterogeneous, usually begin in deep locations inside the material and damage evolution depends on the specific matrix and reinforcement characteristics, the detection of damages in composites is an active field of research [3]. Up to now, a wide range of Nondestructive Testing (NDT) techniques and devices have been used for flaw detection in composite materials, aiming to overcome the challenge of inspecting a broad variety of shapes and internal structures while assuring reliable results [4]. NDT refers to the utilization of various technical methodologies to inspect components and materials without affecting their prospective operational properties and functionalities. These inspections are performed with several objectives, including the measurement of geometrical and physical properties, the characterization of material composition, and the detection, location and evaluation of flaws and damages [5]. Regarding the inspection of structures made of Fiber Reinforced Polymers (FRPs), several NDT techniques have been implemented, including ultrasonic testing, vibration methods, shearography, thermographic testing, radiography, acoustic emissions, digital image correlation, eddy currents, nonlinear acoustics, electrical resistance techniques, and combinations of them [3,6]. In addition, alternatives contained in the field of Structural Health Monitoring (SHM) are being intensively explored. SHM is considered a promising field to address the need for online monitoring and damage detection in composite airframes, a process that can improve safety and diminish maintenance costs in aerospace structures [1]. SHM can be defined as the integration of sensing devices in a structure (alongside with the possible addition of actuating devices) to record and analyze its loading and damaging conditions. This is carried out with the aim of performing an effective localization and assessment of these conditions and the prediction of structural behavior, achieving to have an integral usage of NDT in structures and materials [7]. Thus, changes in the structure affecting its desired performance are identified and corrective actions can be performed in a timely manner, lowering the risk of failures and extending the life cycle of components. SHM includes both diagnosis of structural integrity and prognosis of the repercussion of damages present in a structure. Up to now, most of the developments have been made in the diagnosis field [2]. This is also often represented as a series of levels in which SHM is accomplished, including load monitoring, damage detection, damage localization, damage quantification and prognosis of remaining life [7,8]. There are different SHM techniques including approaches based on vibration, elastic waves, electromechanical impedance, comparative vacuum (CVM) and strain monitoring [9,10]. Perhaps the most mature forms of SHM within the context of aeronautical components and structures are the Health and Usage Monitoring Systems (HUMS) used by the rotorcraft industry, which evolved from condition monitoring of rotating machinery [11,12]. Such approaches for SHM in aircraft have been focused on vibration-based SHM, where vibration metrics are explored based on the principle that structural damages change vibration parameters such as natural frequencies, vibration modes, structural damping, etc [13]. Vibration metrics are capable of providing damage identification in a global level; however, these techniques commonly pose a challenge when are implemented on real structures due to noise and measurement errors associated with environmental and operational condition variability [14]. Elastic waves-based SHM provides potential applications to full-scale aircraft structures with high sensitivity to small-scale defects allowing passive sensing as in acoustic emissions (AE), active sensing as in ultrasonic guided waves or a combination of both as in acousto-ultrasonic techniques. AE are generated by stress waves produced by growth of defects in solids that can be sensed by piezoelectric sensors or Fiber Optic Sensors (FOS) while ultrasonic guided waves and acousto-ultrasonics require the introduction of waves by transducers at specific points, which are sensed by other transducers at different positions of the structure [15]. The main difference between acousto-ultrasonic and guided waves (e.g. Lamb and Rayleigh waves) is the number of mixed modes related to these techniques. Acousto-ultrasonic uses high-frequency impulse excitation with many mixed modes (bulk waves); on the other hand, Lamb waves and other guided waves have an infinite number of modes associated with propagation. In this way, guided waves can interact with small defects in structures even at long distances due to their propagation properties [16]. In composite laminates, propagation of Lamb waves stands out due to their proven suitability to real-time damage monitoring [17]. Recent studies in elastic waves-based SHM include commercially-available SMART LayerÒtechnologies used to locate different damages in wing and fuselage composite skins [18,19], FOS utilized to detect strains from acoustic emissions due to impact-caused failures in composite materials [20], a combination of laser-scanning-based guided wave ultrasonic propagation imaging, FOS and piezoelectric sensors in the so-called Smart Hangar concept to perform crack visualization and impact

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localization [21], Wireless Sensor Networks (WSNs) based on acousto-ultrasonic techniques to assess aircraft flutter [22], and WSNs developed to perform impact localization and monitoring in composite structures [23]. Another promising approach is strain-based SHM, which allows assessing structural integrity by acquiring strain data from sensors attached to or embedded into the structure of interest [24]. This approach relies on the fact that structural damages cause local changes in the component’s stiffness. Despite a large number of studies carried out in the context of strain-based SHM in aircraft structures, the majority have implemented SHM tasks in a laboratory level under simulated operational environment. Several works have accomplished in-flight SHM, where different types of strain gauges and FOS have been employed [25,26]. Among these, FOS are the most used due to their low weight, small size, ability to be embedded, fatigue resistance, corrosion resistance, multiplexing ability and immunity to electromagnetic fields, which make them suitable for practical applications [27–49]. However, most of these approaches have addressed only the load or strain monitoring level and not the task of damage detection in an automated way. Strain time series acquired from FOS do not represent a direct damage indication by themselves since variations in the strain field are commonly small and vanish close to the damage. Therefore, it is necessary to implement robust data processing techniques in order to extract valuable information and perform SHM tasks automatically from real-flight strain data. Only a limited amount of works has been carried out within this context. Kressel et al. identified local buckling during landing in an Unmanned Aerial Vehicle (UAV) by tracking changes in the vibration modes by means of Fiber Bragg Grating (FBG) measurements and Principal Component Analysis (PCA) [40]. Ruzˇicˇka et al. proposed a methodology for debonding detection in Ultralight Phoenix Air U-15 based on the identification of variations in the correlation between FBG measurements and flight parameters; however, until now, it has not been reported tests under operational conditions and with damage scenarios [41,42]. Terroba et al. developed a methodology based on the calculation of a damage index from variations in the slope of the plots strain versus load using calibrated weights as a load source demonstrating suitable results when tested on ground [45]. However, the methodology proposed by Terroba et al. implies the evaluation on ground using the calibrated weights and cannot be seen as an in-flight procedure. Aiming for automatic damage detection from measured data, the statistical pattern recognition paradigm has been profusely used in the last decades [50]. Indeed, the use of pattern recognition has allowed the implementation of strain-based SHM to detect damages globally in contrast with the first approaches of these techniques restricted to local damage detection (e.g. debonding, delamination, matrix cracking and so on) for the purpose of hotspot monitoring (in the vicinity of the sensor). The global damage detection method relies on the fact that loads and strains are redistributed in a structure by a damage occurrence. Therefore, slight changes in the strain field can be detected by using a network of sensors and studying the correlation among measured data to unveil changes in the global stiffness. This has been known as strain field pattern recognition or strain mapping [51]. However, there are limitations that need to be addressed before real-world application of this technique in full-scale structures. In the case of flight vehicles, one of the most important challenges of developing effective, global strain-based SHM systems is the fact that structures are subjected to variable environmental and operational conditions, which produce changes in the strain field similar to those promoted by damage occurrence. Changes in the structural behavior related to operational conditions are difficult to isolate from those related to damages, making necessary the use of decoupling procedures to perform damage detection in a reliable way [52]. In previous works, the authors have addressed this problem by developing a damage detection methodology consisting mainly of the implementation of a so-called Optimal Baseline Selection (OBS) procedure using Density-Based Simultaneous Two-Level Clustering (DS2L-SOM), an unsupervised classification technique based on Self-Organizing Maps (SOM), feature extraction and modeling based on PCA and decision-making based on damage indices. This methodology has demonstrated satisfactory results in a representative structure made of metallic materials under simulated varying operational conditions [53]. The main novelty of this work is validating the aforementioned damage detection methodology in a real-world aircraft. In order to accomplish this, a HUMS based on a lightweight, miniature data acquisition and transmission system was developed and presented as an alternative to performing SHM in composite aerospace structures. The system was implemented in a UAV, also known as Remotely Piloted Aircraft System (RPAS), specifically designed for this research and tested in flight. In order to achieve this, challenges related to implementing an SHM system in an UAV had to be assessed, such as the limited volume and weight available for the equipment in the aircraft, the location and positioning of the system’s components in reduced space, the distribution and selection of the sensors, and the placing of the optical fibers and fragile components for achieving a reliable mounting and stable operation of the system. The front spar of the aircraft’s wing was made of composite materials with 20 FBG sensors embedded in it. A small optical interrogation system was installed in the aircraft as well as a Wireless Local Area Network (WLAN)-based data transmission system, allowing to transmit strain data from the structure during the aircraft’s operation, as an enhancement of the HUMS to allow remote monitoring and data management. Several flight tests with different maneuvers to induce different operational conditions were performed with the structure in a pristine condition and with different realistic damage scenarios (artificial damages). After considering some variations to adjust the methodology for this specific case, where in-flight damage detection in a composite structure is required, strain signals acquired from the flight tests were processed and the results were evaluated using Receiver Operating Characteristic (ROC) analysis to quantify the performance of the whole methodology.

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Thus, by means of this development, it was possible to perform both strain monitoring and automatic damage detection applied to an operating aircraft under real-flight testing with the capability of remote sensing through the implemented transmission system. Despite the fact that the methodology was tested in a UAV platform, since it provides real-flight scenarios with the flexibility and low-cost needed for the development of these SHM methodologies, this concept can be easily transferable to full-scale aircraft and other types of structures (e.g. wind turbines, civil structures, geotechnical structures, etc). This paper is divided into seven sections. In Section 2, the fundamentals of the FOS are introduced, in Section 3, a complete review of the reported work in the field of data-driven models for SHM is presented. Then, an overview of the proposed damage detection methodology is detailed in Section 4 in conjunction with background of the used algorithms. The HUMS design and the setup of the flight tests are described in Section 5, the results and discussion are presented in Section 6, including the HUMS performance evaluation and ROC analysis for assessing the damage detection methodology. Finally, in Section 7, the concluding remarks and future work are stated. 2. Fiber optic sensors Both the invention of laser at the 60’s and the technological advances in the optical fiber (transmission channel) revolutionized the field of telecommunications. Optical fibers can guide a light beam through large distances, this is possible as total internal reflection occurs due to fiber constitution: the fiber has a core with a given refractive index and a cladding with a lower one [54]. When a beam of light is incident from the core to the cladding with an angle greater than a critical value defined by Snell’s law (as a function of the core and cladding refractive indices), refraction of light does not occur, and the signal is kept inside and transmitted towards the core [55]. In the recent decades, the need for faster and better communications around the globe has been the main driver for the research and development of these fibers. As a result, it is up to now the preferred technology for fast and reliable data transmission overseas [56]. Other technological fields have found a place for optical fibers, such as instrumentation, sensing, and control, where characteristics like immunity to electromagnetic interference, lightweight, relative ruggedness, multiplexing capability, wide bandwidth, small size, and passive operation are desired and provided by these fibers [57]. A FOS can be defined as a device capable of sensing one or several parameters that define light as an electromagnetic wave, such as phase, intensity, wavelength or polarization. Consequently, the principle of operation is based on that an external stimulus (e.g. mechanical, thermal, chemical) changes one or more of the parameters that define the optical wave. Based on the kind of these changes, different types of FOS have been developed over the last decades. A general topological classification includes extrinsic and intrinsic sensors [58]. Extrinsic sensors consist of a modification of the optical fiber during a short length to make it sensitive to strain, temperature or another variable. Intrinsic sensors commonly use the inherent defects of the optical fiber to measure different backscattered spectral components including Rayleigh, Brillouin and Raman bands. Through the backscatter dispersion, it is possible to calculate the temperature and strain distribution in a plain communication-grade optical fiber without the need of inducing local changes into the optical fiber [59]. A popular kind of extrinsic, wavelength-modulated sensor is FBG, which consists of periodic variations of the refractive index that are inscribed inside the core of an optical fiber. When light is transmitted through the fiber, a portion of the spectrum is reflected by the grating, which is equal to twice the pitch or period of the modulation in the refractive index times its effective mean refractive index [57]. When the optical fiber is subjected to strain, the space between the modulation of the refractive index (grating pitch) is changed due to fiber elongation or shortening, generating a shift on the Bragg wavelength reflected at the location of the FBG. Also, a slight variation of the refractive index occurs associated with a Poisson’s effect due to dimensional changes in the radial direction. Additionally, FBGs respond to temperature which causes two different effects: firstly, temperature changes cause thermal expansion and the grating pitch changes, and secondly, the refractive index itself is temperature dependent [58]. Both strain and temperature effects in the reflected Bragg wavelength can be calculated using Eq. (1).

dk ¼ k
D
þ kT DT; k0

ð1Þ

where dk is the reflected wavelength shift, k0 is the initial wavelength, D
the change in the strain and DT the change in temperature. k
and kT (given by the fiber manufacturer) are the strain sensitivity and temperature sensitivity of the FBG, respectively. Thus, changes in strain over time can be then measured by tracking properties of the reflected wavelength with an FBG interrogator and by compensating the effects of the temperature in this variable. When compared with typical strain gauges, the utilization of FBGs has advantages in terms of size (around 125 lm for single-mode fibers) and their high capacity of being multiplexed [57]. Because of these sensor’s characteristics, they can be easily embedded into the matrix of a composite material or bonded to its surface. A single optical fiber line with several FBGs can be placed onto large parts such as aircraft main structural components, allowing to gather information from distant and access-limited locations. They even can be implemented to monitor the curing of polymeric-matrix composite materials and determine the residual strains after the material manufacturing [60]. This makes FBGs as suitable candidates to be the source of information of HUMS for in-flight SHM [61].

Table 1 Review of in-flight SHM using FOS. Country

Aircraft

Weighta[kg]

Wingspan [m]

Type of sensor (number)

Location

Data processing

Ref.

1993 2007 2008

Germany United States United States

Cessna C207A Mini UAV developed (No name) Predator-B UAV

1724 1.4 4763

10.92 1.52 20

Michelson interferometer(1) FBG(2) FBG(2880)

Wing Winglet Wing

[26] [27] [28,29]

2010

Spain

SIVA UAV

–

6

FBG(15)

2014 2014

Israel Japan

Heron MALE UAV Mitsubishi MU-300

1150 6636

16.6 13.26

FBG (54) BOCDA(1)

None None

[31–33] [34,35]

2015 2011–2015

Republic of Korea India

Ultralight JARIBU UL-D Nishant UAV

450 550

9.4 6.5

FBG(34) FBG(16)

Wing, fuselage, stabilizer and landing gear Wing and tail booms Fuselage and vertical stabilizer Wing Tail booms

None None Wing deflection estimation None

[36] [37–40]

2017

Czech Republic

Ultralight Phoenix Air U-15

295

15

FBG(102)

Wing, horizontal and vertical stabilizer

2017 2017

Spain Australia

MILANO UAV DJI S900 hexacopter UAV

900 8

12.5 –

FBG(70) FBG(12)

Wing and fuselage Arm

2018

Spain

DIANA UAV

–

–

FBG(4)

Fuselage

2018–2019

Japan

Cessna Citation Sovereign

13959

19.24

OFDR-FBG (2)

2019 2019

Republic of Korea Spain

Flight Design CTLS-ELA CATEC UAS Locomove

7470 2000

600 4.2

FBG (12) FBG (5)

Fuselage, bulkheadand wing Wing Wing

None Fourier analysis, load monitoring during flight by using ANNs and tracking of vibration modes by using PCA Correlation of relationship between FBGs and flight parameters (proposed) None Fast Fourier Transform estimation Calculation of a damage index based on variations in the slope strain versus load None Load estimation Strain monitoring and wing deflection estimation

[48] [49]

[30,43]

[41,42]

[43] [44] [45]

[46,47]

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a

Year

Maximum takeoff weight.

5

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In Table 1, it is presented a literature review of the reported HUMS tested in aircraft during flight using FOS. Important details are described such as aircraft characteristics (i.e. name, weight, wingspan), location of sensors, type of sensor and data processing performed to the acquired signals. As can be seen, different types of FOS have been utilized such as Michelson interferometers, Brillouin Optical Correlation Domain Analysis (BOCDA)-based sensors, Optical Frequency Domain Reflectometry (OFDR)-FBGs and FBGs; however, the majority of studies have employed FBGs. 3. Data-driven models for structural health monitoring A damage assessment methodology should handle the large datasets acquired from the sensors for converting them into meaningful information. Different techniques have been used for this purpose. The early ones involved the use of physical models to describe the relationship between actions and responses in the structure. However, this becomes challenging in aircraft structures due to system complexity and operational and environmental variability [11]. That is why, with the advent of machine learning algorithms, a branch of Artificial Intelligent (AI), the use of data-driven models has increased. In this case, the structural behavior is learned based on experience (or past data), which allows inferring the structural integrity efficiently, having less uncertainty and without needing complex physical models. These algorithms seek to generalize the pristine structural condition in order to detect deviations, from the baseline condition, associated with damages [50]. Although a powerful tool, data-driven models, considering their non-physics nature, must undergo rigorous validation and testing in order to both set training parameters and judge the generalization performance with data not used in the training process [11]. The data-driven approach for SHM, also called statistical pattern recognition, can be seen as a series of four procedures: operational evaluation, data acquisition, feature selection and statistical modeling for feature classification. In the latter procedure, machine learning deals with the process of unveiling the relationship between the selected damage-sensitive features and the structural condition of the component. The learning process can be performed in a supervised or unsupervised way. In supervised learning techniques, the data come from multiple classes and the labels for the data are known, while in unsupervised learning techniques, there are no labels and information about the data structure is unknown [11]. Often, supervised learning has been restricted to the second, third, fourth and fifth levels of the SHM tasks, namely, damage localization, damage type classification, damage severity quantification and prognosis, and it is not frequently explored for damage detection. Imagine, for example, the case of an aircraft structure, in order to use supervised learning techniques, all possible damage scenarios need to be included in the training dataset to provide a suitable damage detection methodology. This may imply the use of a large number of experimental data or the use of Finite Element Analysis (FEA) to produce such datasets, which may not be a practical approach [11]. On the other hand, unsupervised learning techniques are more commonly used for damage detection in the context of anomaly or novelty detection. Novelty detection implies the building of a statistical model using data from the pristine structural condition (baseline) which is then used to test newly acquired data for detecting deviations, related to damage occurrence [11]. The problem associated with these techniques in real-world aircraft structures is the need for a great amount of data containing all the operational conditions that the structure will experienced during flight. If this condition is not assured, an operational condition no present in the training data will lead to false damage indications known as false positives. In opposition, a large dataset containing high variability decreases the performance of the methodology since the built model is too broad so that deviations caused by damages may fit the model and no detection may be produced. Therefore, dealing with the uncertainty posed by the environmental and operational variabilities is a matter of concern in these techniques that is even more evident in aircraft where dynamic loading, load condition variations due to maneuvers and sudden changes in the environmental conditions belong to the routine of flights. Several machine learning algorithms, both supervised and unsupervised, or combination of them, have been used for structural diagnosis and information management in SHM tasks. Such methodologies are based on Bayesian classification, kth-nearest neighbor rules, ANNs, PCA, Fuzzy Logic Systems, Support Vector Machines (SVM), Hidden Markov Models, Mahalanobis Squared Distance (MSD), Gaussian Mixture Models (GMMs), and more recently Deep Learning [50,62]. The development of such methodologies based on data-driven models for automatic damage assessment has been led by vibration-based SHM, which implies the analysis of changes in the modal parameters of the structure. Recent researches involve the use of Multiway PCA (MPCA) and Discrete Wavelet Transform (DWT) for damage detection and classification in mechanical structures using PZTs with an Area Under ROC Curve (AUC) of 0.9988 [63], least square SVM using a novel combinational Kernel for damage detection from acceleration response signals tested in two different civil structures (i.e. a four-story steel structure and a 120-bar dome truss) with a damage detection accuracy of 1 and 0.9814, respectively [64], 1D Convolutional Neural Networks (CNNs) for real-time damage detection and localization in a grandstand simulator by using accelerometers [65], cross-correlation signals to enhance PCA-based piezodiagnostic methodologies experimentally validated in mechanical and aeronautical structures [66], enhanced GMM-based method for damage detection using PZTs demonstrating robustness against environmental and operational condition variations [67] and Condition Indicators (CI) for novelty detection based on Support Vector Data Description (SVDD) in drive train components of a servicing helicopter [68].

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In the case of FOS-based SHM systems, automatic damage detection methodologies have been developed mainly for SHM systems using FBGs to measure dynamic response of the structure. Panopoulou et al. [69] implemented an ANN for structural damage identification in a composite panel using FBG measurements, obtaining a ratio of 0.86 of test vectors correctly classified. Loutas et al. [70] developed an intelligent expert system using DWT, SVMs and Independent Component Analysis (ICA) for damage identification in a flat stiffened composite panel. Similarly, Lu et al. [71] utilized FBGs to measure the structural dynamic response signals together with a methodology based on Fourier transform and PCA as feature extraction techniques, one-class SVM for damage detection (with an accuracy of more than 0.9) and a multi-class C-SVM for damage location and quantification in a Carbon Fiber Reinforced Polymer (CFRP) plate. Neu et al. [72] introduced a multi-stage clustering approach for Automated Operational Modal Analysis (AOMA) using measurements from FBGs and piezoelectric sensors placed on a composite beam under wind tunnel testing. Park et al. [73] designed and tested an SHM system bonding four FBGs to the inner surface of the leading edge in a UAV’s composite wing. A state-of-the-art interrogator, capable of sample strain signals at 100 kHz, was used to measure structural vibrations and strains from the FBGs during bird strike testing. The frequency characteristics of the acquired data were extracted with the short-time Fourier transform and the results were processed with an Artificial Neural Network (ANN) algorithm to estimate the locations of the strikes, obtaining an average error of 33.6 mm in the location. Gupta et al. [39] developed an SHM procedure by embedding FBG sensors into a representative aircraft structure made of CFRP. Different load cases were tested with the structure in pristine and damaged conditions, presenting the acquired data to two different ANNs, one to estimate sensor malfunctions and other to analyze the structural integrity regarding disbond damages and the estimation of the loads in the structure. The root mean square error for total load estimation was 7% and for disbond location was 2.1%. Further testing of this system was carried out in a UAV testbed with successful load identification from the algorithm. Kim et al. [74] implemented a system to monitor the structural health based on FBG sensors attached to the wing of a solar-powered aircraft. The structure was tested with load and heat application; strain, damage and acoustic emissions monitoring were performed, achieving a successful damage identification and location (with an error of approximately 14% between the estimated location and the actual one) by processing the data gathered from the sensors. Park et al. [75] designed an advanced on-board, off-line SHM system for a UAV based on FBG and PZT sensors, capable of detecting damage occurrence and monitoring the strains and acoustic emissions of the structure. The development was tested on ground using a full-scale wing section with known damages. Damage localization and severity estimation were carried out with an ANN. However, most of the researches related above deal with laboratory tests of representative aeronautical structures and do not involve in-flight SHM. Indeed, in-flight SHM using FOS has been accomplished in the context of HUMS. According to Yan et al. [62], HUMS is a combination of health and usage monitoring to provide accurate information regarding the condition of various flight critical components. The health monitoring portion seeks for damage detection while usage monitoring seeks for load monitoring and fatigue life estimation [11]. The most important aspect of those systems are the efforts that have been carried out to successfully implement them in operating aircraft, considering integration and certification issues promoted by rotary-wing aircraft [76,77]. For a detailed description of these systems the reader is directed to [12]. As mentioned before, most of these in-flight studies only address the level of load monitoring by strain sensing and the information is gathered for off-line, human-based analysis. Table 1 also shows the data processing methodologies implemented for in-flight data; however, as discussed in Section 1, these have limitations related to flight phase applicability, type of damages detected, test conditions and robustness under operational condition variations. In previous works by the authors, it can be observed the development of the so-called strain field pattern recognition methodology with applications at laboratory level of a 1.5-m wing section of a UAV made of composite materials [78], a lattice spacecraft structure made of CFRP [79] and a 13.5-m long wind turbine blade made of composite materials [80]. This is based on PCA to study correlations among sensors to reconstruct the strain field in the structure for a determined state. Early proposed for elastic-waves based SHM by Mujica et al. [81], this approach differs from the traditional use of PCA in SHM, when commonly just the first components or the projections to these principal components are analyzed, since PCArelated statistics are used as damage indices to infer the structural state [63]. However, a robust in-flight damage detection methodology must account for environmental and operational condition variations expected in real-world aircraft structures. Since strain field changes promoted by damages are small, it is necessary to decouple them from changes in the strain field promoted by operational conditions. One alternative explored by the authors is to use clustering, or unsupervised classification, which is defined as a partitioning process where datasets are divided into subgroups having common features or similar patterns [82]. This consists of a so-called OBS procedure using DS2L-SOM for creating clusters representing different operational conditions before performing feature extraction and final classification using PCA and its associated statistics (damage indices). The methodology was successfully proved in an aluminum beam (a representative aircraft structure) under simulated operation (dynamic loading and discrete pitch angle changes) [53].

4. Proposed damage detection methodology The damage detection methodology proposed in this work for in-flight damage detection follows the common steps of the statistical pattern recognition paradigm proposed by Farrar and Worden [11]: data preprocessing for standardization and

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cleaning, OBS based on DS2L-SOM for dealing with environmental and operational variability and PCA in conjunction with damage indices for feature extraction and statistical modeling. This section aims to briefly summarize the methods behind the implemented methodology. For a further insight and a deep theoretical background involving the used algorithms, the reader is directed to [53]. 4.1. Data preprocessing Data preprocessing refers to the process of eliminating outliers (filtering) and scale variables to increase the performance of the pattern recognition techniques. In PCA, as in many data-driven modeling techniques, outliers have a deleterious impact on its efficiency. Since, from definition, both covariance matrix and variance are sensitive to outlying observations, the output data after PCA may become unreliable in the presence of outliers [83]. In addition, considering that input variables may have different magnitudes, data standardization is also important to eliminate this non-damage-related variability produced by load magnitude variations presented in the flight tests. For the aluminum beam experiment under cyclic loading reported in [53], filtering and outlier detection techniques such as low-pass filtering and local maximum extraction based on peak detection demonstrated to give satisfactory results. These techniques are based on the signal form, however, considering that the expected in-flight strain signals are not periodic and may contain several peaks due to sudden motions, other techniques based not on the signal form but in the data distribution were explored. Kernel Density Estimation (KDE) and Hampel identifier are two well-known techniques used in signal processing and machine learning for data cleaning and outlier detection. In the case of KDE, this technique has been widely used for the purpose of outlier detection in SHM. Examples include its experimental validation for damage detection in a simplified model of a metallic aircraft wingbox [84] and its application after PCA to enhance the interpretation for process monitoring [85]. Through this technique, it is possible to obtain the Probability Density Function (PDF) for the feature variables (i.e. strain measurements). Low PDF magnitudes can be viewed as an indication of a greater degree of outlierness [86], thus, data are discarded when they are out of a determined confidence level (i.e. 95%, 99% and 99.5%). Spikes and measurement errors are expected to be removed after this filter since, from definition, the probability to occur is low. The kernel density estimate, ^f
X , with kernel K is defined as follows: N X
 ^f
X  ¼ 1 K X  Xi ; N i¼1

ð2Þ

where X i is the ith point in the data and N is the number of points. In this work, it was used the algorithm of multivariate KDE via diffusion developed by Botev et al. [87], which utilizes a plug-in bandwidth selection method and assumes the kernel to be Gaussian. On the other hand, Hampel identifier relies on simple statistical metrics such as the mean and the variance of the data to detect deviations. These metrics are estimated using a moving window for taking into account the local distribution in the data instead of estimating them over all the samples [88]. This technique is particularly useful in this application since through the moving window local outliers can be identified, for example, a strain spike occurring in the climb stage is compared with its respective neighbors instead of using all flight stages where may fall within the confidence interval due to a higher variance. Application of this filter has been led by the statistical process control field where is regarded as one of the most robust and efficient outlier identifiers [89,90]. Additionally, Zhao et al. [91] recommended the use of Hampel identifier and boxplot rule for solar photovoltaic fault detection. Marti-Puig et al. [92] evaluated the use of filters such as Hampel identifier for fault diagnosis in wind turbines demonstrating a decrease in the training error but an increase in the testing error when using a normality model based on partial least square. Regarding data standardization, several techniques have been implemented such as global maximum strain, local maximum strain and average strain standardization [81,53]. The standardization, independently of the used technique, consists of dividing each vector sample (containing all the features for an instant of time) by a specific feature or a combination of them. In the case of global maximum strain standardization, the vector is divided by the sensor having the maximum strain. Local maximum technique is similar to the global standardization but divides the data according to the local maximum strain of their corresponding line (i.e. tension, compression, positive torsion, negative torsion). Average standardization divides each sample vector by the median of all their features (sensor readings). 4.2. OBS based on DS2L-SOM OBS consists of clustering pristine structure data in groups with similar strain patterns associated with a similar operational condition to create different baselines. For example, strain patterns obtained when aircraft is in straight flight with undeflected control surfaces are expected to be different from those obtained when elevators are deflected to a given angle without necessarily implying damage. Then, acquired data for an unknown condition are associated with their optimal baseline by means of an inverse clustering process. In case of no similarity with any of the baselines, such data is treated as an anomaly and classified within the presented methodology as a damage. Conversely, if there is similarity between such data

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and any of the clusters (baselines), they are projected onto the statistical model associated with this condition. Thus, instead of a general baseline or statistical model, there exist different baselines considering the different operational conditions presented [53]. The OBS procedure uses DS2L-SOM based on the learning of a SOM for data clustering. SOM is a widely used unsupervised learning technique based on ANNs for dimensionality reduction, data visualization and pattern recognition. In its simplest form, SOM allows converting complex nonlinear relationships in high dimensional spaces into simple geometric relationships of their image points on a reduced space, usually represented by a two-dimensional space (grid) [93]. However, the clustering of the data cannot be obtained directly by means of this technique. Two-level clustering approach based on SOM seeks to reduce computational cost and noise by computing a set of reference vectors, or prototypes, representing local averages of the data prior to using conventional clustering algorithms [94]. Cabanes and Bennani [82] developed a so-called DS2L-SOM procedure that combines the dimension reduction of SOM (in the first level) with the simultaneous learning of the data structure and their partitioning by means of both density and distance information. The algorithm assumes, thus, that a cluster is a high-density region of reduced space surrounded by a low-density region. This procedure is based on the extraction of information from the unified distance matrix, known as U-matrix, a visualization SOM tool, which gives a representation of the average distance of all cells to their neighboring cells. The main idea is that weight vectors associated with SOM neurons having large U-values, represent large distances from the input dataset. Conversely, weight vectors associated with neurons with small U-values, represent short distances from other vectors in the input dataset. Then, such weight vectors are re-grouped for creating the borders of the final clusters [53]. The main advantage of this procedure is that is fully unsupervised, namely, the number of clusters k do not need to be determined by the user. Instead, the optimal number of clusters is learned in the training process. This is a robust way of dealing with the so-called model selection problem. Such fully-unsupervised capability of the DS2L-SOM is key for the application presented in this paper since it is not possible to determine a priori how many operational conditions involving different strain patterns can occur in flight (number of clusters), where complex and different load conditions are expected. For a further description of the DS2L-SOM and the mathematical procedure for automatically learning the optimal number of clusters, the reader is directed to [82]. Another advantage of DS2L-SOM related to the model selection problem is the existing framework for automatic training parameters selection developed by Vesanto et al. [95] for SOMs. Number of map units is determined by using the heuristic pffiffiffi formula, munits ¼ 5 n, where n is the number of samples. Then, map size ratio is set to be the ratio between the biggest eigenvalues of the covariance matrix of the input data, in this way, the map size is calculated so that, keeping that ratio, the number of map units are as close as possible to the determined value [94,95]. In addition to the map size, the topology factors (i.e. local lattice structure and global map shape) must be specified. Local lattice structure gives the connectivity of the maps units, which can be hexagonal or rectangular. In terms of the global map shape, it can be a sheet, a cylinder or even a toroid. In this work, hexagonal lattice structure and sheet map shape were used for ease and considering the successful results obtained by Tibaduiza et al. [96] with the same choices. 4.3. PCA and damage detection indices PCA has been widely implemented to perform SHM tasks for different purposes [40,71,81]. It is a well-known, nonparametric statistical tool that allows dimensionality reduction while maintaining the major variability of the data [97]. Also, PCA permits information extraction by finding a combination of variables or factors that could exhibit major trends in a dataset [81]. Mathematically, PCA involves the estimation of the covariance matrix of the dataset X ðn  mÞ (where n is the number of experimental samples and m the number of variables), which gives a measure of the linear relationship among variables. Then, the eigenvectors and the eigenvalues are calculated from the covariance matrix. The eigenvectors are sorted out from the highest to the lowest as a function of their associated eigenvalues. The eigenvector having the highest eigenvalues is the principal component of the dataset. Thus, the matrix composed of the desired retained number of eigenvectors represent a statistical model (or PCA model) of the data and by projecting the original data over the direction of the retained components the transformed data matrix can be obtained [83]. For a more detailed mathematical background see [97]. The parameter to select is p, the number of retained components. Different methods have been developed to select this parameter accordingly. One commonly used method presented by Deraemaeker and Worden [98] is to select p so that the retained components account for a determined variability calculated as in Eq. (3).

Pp 2 ri I ¼ Pi¼1 ; l i¼1

r2i

ð3Þ

where r2i are the associated eigenvalues and l the rank of the covariance matrix. Then, p is the lowest integer whereby a desired variability threshold is achieved. Commonly used threshold values are 90%, 95% or 99%. Since there is not a widely accepted rule to define such threshold, the selection requires some engineering knowledge, in previous experiments [78–80], it was demonstrated that is suitable for the proposed damage detection methodology to retain the number of components p accounting for a 95% of the variability.

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Damage indices are statistics derived from the PCA process to determine whether consistency among the data exists. The two most used are the Q index, also known as Squared Prediction Error (SPE), and the T 2 index, also called D-statistic [81]. Mathematically, Q index of each datum is estimated based on the perpendicular distance to the PCA model hyperplane. On the other hand, T 2 index is calculated based on the distance between the datum to the model center on the hyperplane. Hence, Q index is able to detect discrepancy outside the PCA model and T 2 discrepancy inside the model. 4.4. Methodology in practice It is important to mention that this methodology aims to be suitable for practical applications. Therefore, one of the design drivers was to follow an unsupervised approach and avoid the requirement of data from damage cases for the training process. Most of the machine learning algorithms require labeled classes (i.e. pristine cases and different damage cases) for validation involving training parameters selection, however, in practical applications these data can be difficult to obtain and, if possible, they may fail to represent all possible damage types and locations. That is why relying on these data for parameter tuning may lead to a lack of generality or models only valid for the types of damages represented by the training and validation datasets. Although some variations can be included, the steps of the whole methodology are almost the same [53]: 1. Use data preprocessing techniques for cleaning and noise filtering (e.g. data having low signal-to-noise ratio, outliers, sensor reading errors, etc.) to improve the performance of the machine learning algorithms. 2. Unfold and standardize the data using appropriate techniques depending on their distribution (e.g. zero mean and unity variance, maximum strain standardization, etc). 3. Train the SOM algorithm with the baseline data containing the different operational conditions. 4. Use the DS2L-SOM for automatically identifying the clusters from the U-matrix derived from the SOM step. 5. Build dimensional-reduced models using PCA for each one of the created clusters. 6. Define damage thresholds using a desired confidence level. 7. Follow step one and two with data coming from an unknown condition (newly acquired data). 8. Perform the OBS methodology by means of an inverse DS2L-SOM to associate the newly acquired data into one of the existing clusters, which physically means identification of the operational condition. 9. Project the newly acquired data onto the PCA model corresponding to their cluster. 10. Calculate the damage indices based on whether consistency with the model exists. 11. Compare the calculated damage index with its respective damage threshold and make the final classification (decision making). For the aim of this work, different variations were considered. The first one consisted of testing filtering and outlier techniques previously described (i.e. low-pass filter, local maximum extraction, KDE and Hampel identifier) in conjunction with different standardization techniques (i.e. global maximum strain, local maximum strain and average strain standardization as well as no standardization). Regarding step number five, it was considered PCA and no one of its nonlinear alternatives due to it is not expected nonlinearities in the data since, in this case, the structure was made of composite materials, being a material with a linear behavior, and with the design driver of avoiding local buckling. In other case studies, where nonlinear phenomena were evidenced (e.g. local buckling, large displacements, etc.), the authors used Hierarchical Nonlinear PCA (h-NLPCA) to obtain robustness against such nonlinearities [80,53]. Finally, for step number ten, Q index was selected to perform the final classification considering that, in previous experiments, it has been demonstrated to be the most sensitive to detect anomalies among other indices. On the other hand, T 2 index has not shown a consistent suitable performance for damage detection, conversely, it can be used as metric to determine if the model is well defined since from definition measures the variability inside the model [78–80]. Within the context of machine learning classifiers, the base of this damage detection methodology, it is important to define tools for evaluating the accuracy of the final classification. ROC analysis, confusion matrix and their associated metrics are commonly used for this purpose. These are based on the rate of samples that the methodology classifies correctly and incorrectly; therefore, four outcomes are possible: True Positive Rate (TPR) or recall, True Negative Rate (TNR), False Negative Rate (FNR) and False Positive Rate (FPR) [99]. Additionally, several metrics (each one having different advantages and disadvantages) have been developed based on the above in order to provide a single metric to an easier and broader performance evaluation. These metrics include accuracy, precision and F 1 score (also called F-measure or F-score). Accuracy, defined as the ratio of correctly predicted classifications to the total number of classifications, is one of the most widely used metrics. However, when dealing with unbalanced data (i.e. when pristine cases outnumber damage cases), accuracy fails in providing a holistic vision of the performance. Recall or TPR does not account for false alarms and it is strongly affected when damage cases are incorrectly classified as pristine cases, this metric is one if all the actual damages are classified as damages. On the other hand, precision (ratio of true positives to the sum of true positives and false positives), does not account for false negatives (or misses), this metric is one if all the cases classified as damages are actual damages. Both false alarms and false negatives are undesirable in practical applications within the context of SHM systems; consequently,

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F 1 score provides an intuitive combination of recall and precision by means of the harmonic mean. That is why in this work, F 1 score was considered in conjunction with accuracy as performance metrics [100]. The Area Under ROC Curve (AUC), was considered as a performance metric as well. However, its results are not presented since it cannot give a global vision of the performance of the methodology. This is explained by considering that multiple models (one per cluster) are created with the proposed methodology, therefore, there are as many AUC indices as number of clusters. 5. Experimental setup 5.1. Health and usage monitoring system Data acquisition and processing systems were developed and implemented in a UAV, named Smart Materials Aerial Platform (SMARP), specifically designed for this research aiming to be reliable, dynamically stable and with a low stall speed to ease control. The UAV is an electrically-propelled aircraft with one pusher and one tractor electric motors for a combined thrust around 10 kg, a wingspan of 3.62 m and a takeoff weight of 15 kg. A depiction of the aircraft is presented in Fig. 1. The UAV was integrated with a mRoTM PixHawk (PX4) autopilot to allow different flight modes such as fully autonomous, stabilized, and manual flight. For the purpose of this research, the UAV was flown in the stabilize mode (which allows to remotely fly the vehicle by an operator on ground with in-flight stabilization). The autopilot system combined several sensors which measure and collect flight parameters to estimate the aircraft state and control it. These sensors are an MPU6000 3-axis accelerometer/gyroscope, an ST Micro L3GD20 3-axis 16-bit gyroscope, an ST Micro LSM303D 3-axis 14-bit accelerometer/compass, a MEAD MS5611 barometer and a 24-bit airspeed sensor. They collect accelerations, angular velocities, altitude, attitude (Euler angles), angle of attack, angle of sideslip, airspeed, surface deflections and temperature. The SHM system was designed to monitor the structural integrity of the wing’s front spar, which is a hollow box rectangular beam made of a balsa core and CFRP skins. The stacking sequence through the thickness of the CFRP is side-dependent as the wing is loaded in a similar way as a cantilever beam: one layer of unidirectional 12 K fibers was laminated following the longitudinal axis at the top and bottom sides (where tension and compression due to bending are the most important loads), and one 3 K-3 K fabric layer was laminated with a [45 /45 ] orientation at the front and back sides (where torsion loads are predominant). SMARP’s wing is divided into three main sections: an inboard trapezoidal section with a semispan of 1.15 m, including the aforementioned composite front spar, an outboard rectangular section with a semispan of 0.51 m

Fig. 1. The SMARP aircraft (all dimensions in mm).

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combining a wooden structure with CFRP, and a wing tip made of expanded polystyrene. The whole structure was covered with a lightweight polymeric shrink film. A depiction of the wing’s structure is presented in Fig. 2. A total of 20 FBG sensors, each one with a grating length of 10 mm and a bandwidth of 0.3 nm (provided by DK Photonics), were embedded at the right semispan of the composite front spar during the manufacturing process. First, the sensors were assembled with a fiber optic fusion splicer into four lines (SMF-28 recoated with acrylate), each one having five FBGs. Then, the lines were bonded to their corresponding side over the balsa core using cyanoacrylate. The FOS lines were located as follows: one line at the top side of the beam for measuring strains due to compression loads (compression side), one line at the bottom face for measuring strains due to tension loads (tension side), one line with the FBGs located at 45 in beam’s front side for measuring strain due to negative torsion loads (negative torsion side), and one line with the FBGs located at 45 in beam’s back side for measuring strains due to positive torsion loads (positive torsion side). The line’s locations and the sensors’ positions are detailed in Fig. 3.

Fig. 2. Wing structure, top view of the right side (all dimensions in mm).

Fig. 3. Sensor locations at each side of the right main front spar all dimensions in mm, sensor wavelengths in nm).

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The specific wavelengths of the sensors were selected using a genetic algorithm to sort an available set of center wavelengths into a configuration where each position of the FBG sensors was distributed inside the spectrum with a minimum wavelength separation of 2 nm between each neighbor, or between the corresponding spectrum ends for the first and last sensors. This wavelength separation was selected considering a safe gap of 1600 l
to avoid overlapping, this value was estimated by preliminary and conservative FEA simulations assuming expected maneuvers of about 3 G. The fitness function for the genetic algorithm is presented in Eq. (4). 19 X
 argmax j k0i  k0iþ1 j; x

ð4Þ

i¼1

  where x is a vector containing the selected center wavelengths x ¼ k01 . . . k020 and the guesses are vectors containing all the sensor’s commercially available center wavelengths k0 . Additionally, the sensors to be located at the torsion sides were selected to be the ones with reflected wavelengths near to the extremes of the spectrum, while compression-side and tension-side sensors were the ones near to the center of the spectrum. This configuration ensures no overlapping of the reflected wavelengths as all of the shifts occur towards the ends of the spectrum. The sensors’ reflectivities were also customized in order to achieve a uniform intensity of the reflected spectrum. As the power spectrum of the light source has a Gaussian-like distribution, and each FBG reflects a specific wavelength; each reflection will have a different intensity if all sensors have the same reflectivity. This phenomenon is undesirable as interrogating parameters like power threshold, peak recognition, and signal gain are adjusted equally for the entire sensors simultaneously, as a single-channel interrogator is used. In order to overcome this fact, reflectivity was customized for each sensor, resulting in uniform-power reflected wavelengths. The difference in the reflected spectrum with and without customized reflectivity is presented in Figs. 4 and 5. Once the sensing lines were bonded to the balsa core (see Fig. 6a), the CFRP layers were laminated with a hand lay-up process, performing the same procedure over the plain balsa at the left semispan of the front spar. Then, the spars were integrated to the wing structure and a special box was installed in the right wing to protect the optical fibers and SC/APC connectors from possible hazard during the aircraft manipulation and operation (see Fig. 6b and 6c). Also, a hand access to the front spar was built at the right wing’s bottom surface for then implementing artificial damages. The optical sensing system is powered by a small-body light source with a wavelength range from 1520 nm to 1570 nm, a center wavelength of 1550.1 nm and a maximum optical output power of 5.1 mW. The wavelength detector is a miniaturized, single-channel FBG interrogator with a 1525 nm to 1570 nm wavelength range, a typical wavelength fit resolution of 0.5 pm, a wavelength accuracy of 5 pm and a maximum interrogating frequency of 6 kHz, capable of detecting up to 37 FBGs at the same time. An optical circulator and a 1x4 optical splitter are also part of the system. The whole system was installed at the main cargo deck of the SMARP aircraft. The information received by the interrogator is decoded at the on-board computer, which is a mini PC running the interrogator’s manufacturer-provided software. The acquired data can be stored locally at the minicomputer for later download to a flash memory or hard drive when the aircraft is on ground. At the same time, a wireless transmission system was designed to provide data streaming from the SMARP aircraft to a ground station during flight tests. The transmission system used a transfer control protocol and internet protocol (TCP/IP) framework over a WLAN to stream wavelength data packages. The network connectivity was achieved using a hardware including air and ground components. The ground components are a 2.4 GHz, IEEE 802.11b/g/n compatible radio with a maximum output power of 28 dBm, connected to an outdoor, omnidirectional antenna that provides a gain of 15 dBi. This equipment constitutes the access point that provides network availability to the ground and aircraft’s on-board computer terminals. Air components of the transmission system are a USB WLAN adapter with a maximum data rate of 150 Mbps, and 500 mW of maximum power, using its own 5 dBi antenna,

Fig. 4. Reflecting FBG spectrum without customized reflectivity.

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Fig. 5. Reflecting FBG spectrum with customized reflectivity.

Fig. 6. Instrumentation and integration of the front spar into the wing structure. (a) Balsa core with sensors bonded on the surface. (b) Spar after hand layup of CFRP skins. (c) Integration into wing structure.

located at the aircraft’s lower fuselage. An overview of both the on-board acquisition system and the ground communication system is detailed in Fig. 7. Data transmission is carried out with a connection between the on-board computer and a ground computer using the TM WLAN. At the on-board computer, the interrogator’s software, written in LabVIEW , was modified to include a module that Ò uses TCP/IP functions to establish a data exchange procedure with a MATLAB code running at the ground computer. At the on-board computer, data are acquired, processed and consolidated into packages containing 100 lines of readings. Each line contains the information of all sensors at the time of a reading. A column containing the time stamp is appended to the package, and then it is sent to the ground computer, where this information is organized and saved to disk for subsequent processing. 5.2. Flight tests A total number of 16 flight tests were carried out, each one consisting of taxiing, takeoff, flying following ellipsoidal patterns, performing different maneuvers to load the structure and landing. This procedure was performed with the aim of acquiring diverse strain data from the sensors, corresponding to the different operational conditions of the aircraft. The achieved interrogating frequency was 100 Hz and, on average, the flight duration was eight minutes. Fig. 8 shows the SMARP UAV during one of these flight tests.

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Fig. 7. On-board and ground systems overview.

In addition to the maneuvers associated with maintaining the ellipsoidal pattern (four banked turns per each complete pattern), specific maneuvers such as pitch motion, roll motion and yaw motion maneuvers were performed. Each maneuver involved cyclic variations over 30 s (in average) of the respective control surface (elevator, aileron and rudder) to produce different loadings in the aircraft wing. Thus, the aircraft climbed and descended, turned right and left and slipped right and left for multiple times in each pitch, roll and yaw maneuver, respectively. Although only in two flights these specific maneuvers were not carried out, in the remaining flight tests each maneuver was repeated twice. Six flights were conducted with the structure in a pristine condition to generate baseline strain field distributions from the acquired information. The remaining ten flights were performed with artificial damages induced in the structure, acquiring data for later comparison with the baseline strain fields by using the developed damage detection methodology based on pattern recognition. Thus, around 35% of the data corresponded to the baseline (BL) and 65% to damage cases. In Table 2, flight test details are shown, including the structure condition (pristine or a specific damage case) and the number of samples, which refers to the number of rows containing 20 strain measurements (features) that were acquired for each flight so that one sample has 20 strain data for a given time. In total, six damage cases were induced by bonding 1-mm-thick steel plates to the right front spar skin and inferior spar skin using a thin layer of silane terminated polymer-based adhesive. These artificial damages cause local variations in the

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Fig. 8. Photograph of the SMARP UAV during flight test number four.

Table 2 Flight tests description (number of samples refers to the number of time instants were the 20 strain readouts were acquired in a flight). Flight number

Identifier

Structure condition

Maneuvers

Number of samples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

BL_1 D1_1 D2_1 D2_2 BL_2 BL_3 BL_4 D3_1 D3_2 D4_1 D4_2 D5_1 D5_2 D6_1 BL_5 BL_6

Pristine structure Damaged structure Damaged structure Damaged structure Pristine structure Pristine structure Pristine structure Damaged structure Damaged structure Damaged structure Damaged structure Damaged structure Damaged structure Damaged structure Pristine structure Pristine structure

No Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

58293 52343 41913 47953 48400 19590 44595 42064 43963 42405 50240 45482 63382 62061 56359 46077

with D1 with D2 with D2

with with with with with with with

D3 D3 D4 D4 D5 D5 D6

front spar’s stiffness, simulating the same effect that produces real damages (reduction in the component’s local stiffness) without affecting the flight safety. The sizes of the steel plates were selected aiming to increase the local stiffness between 5% and 20%. In this way, different strain distributions are generated to be compared to the baseline behavior under similar operational conditions. A detailed description of damage sizes and locations is presented in Fig. 9. Damage case 1 (D1), damage case 2 (D2) and damage case (D4) consisted of single steel plates of different sizes bonded to the positive torsion side (beam’s back side). Damage case 3 (D3) comprised the bonding of two steel plates of different sizes stacked one over the other to the positive torsion side. Without removing the plates of D3, damage case 5 (D5) was generated bonding two plates of different sizes stacked one over the other to the tension side (beam’s bottom side). Finally, damage case 6 (D6) comprised the same setup for D5 using a single plate in the tension side instead of two stacked plates. 6. Results and discussion The HUMS demonstrated a stable operation during the flight tests, without affecting the aircraft operational performance. By means of this system, it was possible to gather information without data losses, sensor damages or system failures. Additionally, the system was able to resist all flight conditions (i.e. taxiing, takeoff, climbing, maneuvers, descent and landing), showing its robustness and reliability. The in-flight strain distribution of the composite structure was monitored by transforming the reflected wavelengths acquired by the interrogator into strain measurements using Eq. (1) with the identified sensitivity coefficients (k
¼ ½0:7991  0:0055  106 l
1 and kT ¼ ½6:33  0:074  106 K 1 ), giving strain measurements with an accuracy of

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Fig. 9. Damage conditions (all dimensions in mm).

±4.04 l
. The temperature measurements acquired by the autopilot were used to compensate temperature effects in the reflected wavelengths and obtain accurate strains. These strain data can be used to obtain load spectra of the aircraft and perform fatigue life estimation. Additionally, overloads produced by anomalous conditions (e.g. maneuvers, hard landings, gust loads, etc.) can be detected by monitoring these load spectra. In overall, the flight tests induced an average strain of 700 l
in the sensor nearest to the root on the compression side (Sensor 10), which had the higher strains. In some maneuvers, this sensor achieved maximum measurements ranging from 1000 to 1300 l
. Sensor 11, the sensor nearest to the root on the tension side, attained average strains of 450 l
and maximum ranging from 700 to 800 l
. As the predominant loading in an aircraft wing is bending due to lift force distribution, lower strains were attained in the torsion sides. In average, the torsion sensors nearest the root (Sensor 5 and Sensor 16) measured an absolute strain of 200 l
and a maximum absolute strain around 300 l
(both positive and negative torsion sides). Regarding the wireless network, its range was tested on ground by placing the access point’s antenna at a height of 1.5 m above the ground. A horizontal outdoor range of 150 m was achieved in all directions, within a field where several obstacles like moving people, various kinds of vegetation, small civil and miscellaneous structures were present. The capability of the wireless transmission system to stream strain data from the sensor-embedded structure during the aircraft’s operation was tested in several flights. The wireless network was set up as follows: the access point was located at the top of a hoarding, a laptop was used as the ground station computer, and the WLAN radio and antenna were installed at the exterior surface of the aircraft’s belly, behind the nose landing gear. Communication was established between on-board and ground computers and data were streamed during the whole flight phases, starting during taxiing and stopping the transmission after landing. Around 8 and 7.5 min of data were streamed per flight consisting of messages delivered in packages of 100 readings per second. The same maneuvers as the

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non-transmission flights were performed, carrying the aircraft until a maximum distance of 572 m away from the access point. The tests were carried out in a test field with no obstacles to the wireless signal. Data streaming was achieved with good stability requiring an average network bandwidth of 200 Kbps, achieving to transmit the 100-messages packages each second without significant delays, in addition, no signal losses were evidenced during the flights. Flight parameters gathered by the autopilot, related to aircraft dynamics, can be associated with the strain data. In Fig. 10, an example of the monitored strains in flight test number four by Sensor 10 (maximum compression side sensor) is shown and compared against the pitch angle measurements. It can be appreciated the strain spectrum, including all the strains induced at the different flight stages and maneuvers. Special mention deserves the pitch motion maneuvers that are magnified at detail A and B (see Fig. 10), which induced high compression strains and are easily identifiable in the strain spectrum. As can be seen, when pitch angle increases (up elevator), higher strains are present in the wing structure. This is due to the fact that pitch angle is related to angle of attack so that larger angles produce an increase in lift. Pitch angle is a variable belonging to the longitudinal dynamics of the aircraft. Therefore, it is expected to have a considerable effect in the strains occurring at the tension and compression side of the front spar as demonstrated in Fig. 10. Conversely, maneuvers involving roll motion do not affect significantly aircraft longitudinal dynamics since it is a variable belonging to lateral dynamics. In Fig. 11, it is shown the acquired strains by Sensor 20 (minimum positive torsion side sensor) and the roll angle for the same flight than in Fig. 10. It was demonstrated no considerable induced strains by the pitch motion maneuvers in this sensor. On the other hand, there is a strong correlation between roll angle changes and strains at this point, which is evident in the roll motion maneuvers magnified at detail C and D (see Fig. 11). These detail views show that an increase in roll angle produces a decrease in torsion strains, this is explained by the fact that it is expected that when aircraft perform a turn to the right (up right aileron and down left aileron producing a positive angle roll), lift is reduced at the right portion of the wing. As lift is reduced, there is less aerodynamic moment producing wing torsion loads and, therefore, torsion strains are reduced as well. These relationships are consistent with the results obtained by Kim et al. [36]. Figs. 10 and 11 are useful not only because they are examples of aircraft longitudinal and lateral dynamics but also they are the measurements acquired by the most critical sensors, which are suitable for analyzing the monitoring system’s

Fig. 10. In-flight strain measurements by Sensor 10 (maximum compression side sensor) and pitch angle measurements during flight test number 5 (BL_2) related to the aircraft longitudinal dynamics (pitch angle variations detailed in A and B are related to pitch maneuvers involving cyclic movement of the elevator).

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Fig. 11. In-flight strain measurements by Sensor 20 (minimum torsion negative side sensor) and roll angle measurements during flight test number 5 (BL_2) related to the aircraft lateral dynamics (roll angle variations detailed in C and D are related to roll maneuvers involving cyclic movement of ailerons).

Table 3 Performance of the proposed damage detection methodology variations (100% of the samples refers to 765120 time instants). Identifier

Filter

Standardization

Samples

Number of clusters

RAW_NO_OBS RAW_OBS RAW_GLOBAL RAW_LOCAL RAW_MEAN KDE_95 KDE_99 KDE_99.5 KDE_95_GLOBAL KDE_95_LOCAL KDE_95_MEAN HAMPEL HAMPEL_GLOBAL HAMPEL_LOCAL HAMPEL_MEAN

None None None None None KDE 95% KDE 99% KDE 99.5% KDE 95% KDE 95% KDE 95% Hampel Hampel Hampel Hampel

None None Global maximum Local maximum Mean None None None Global maximum Local maximum Mean None Global maximum Global maximum Local maximum

100% 100% 100% 100% 100% 77.19% 92.58% 94.78% 77.28% 77.11% 77.11% 90.76% 90.72% 89.76% 90.31%

1 126 3 3 4 127 119 117 1 1 1 120 2 2 2

F 1 score 95% 0.869 0.964 0.794 0.784 0.187 0.985 0.981 0.975 0.672 0.087 0.817 0.978 0.781 0.781 0.211

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.003 0.002 0.009 0.002 0.021 0.002 0.002 0.003 0.008 0.026 0.042 0.002 0.001 0.001 0.068

Accuracy 99%

0.765 0.941 0.062 0.080 0.104 0.978 0.967 0.958 0.370 0.061 0.613 0.959 0.040 0.021 0.118

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.011 0.003 0.009 0.032 0.022 0.003 0.003 0.005 0.014 0.026 0.059 0.004 0.027 0.004 0.078

95% 0.849 0.955 0.668 0.645 0.415 0.981 0.975 0.968 0.692 0.408 0.809 0.972 0.641 0.641 0.435

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.003 0.003 0.022 0.002 0.009 0.002 0.002 0.003 0.006 0.011 0.038 0.002 0.001 0.002 0.030

99% 0.754 0.930 0.370 0.378 0.388 0.974 0.959 0.948 0.489 0.398 0.637 0.948 0.372 0.364 0.402

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.0092 0.0038 0.0033 0.0103 0.0082 0.0038 0.0031 0.0062 0.0060 0.0108 0.0366 0.0053 0.0098 0.0021 0.0316

stability. Sensor 10 is the nearest FBG to the wing root of the compression side of the spar; therefore, the higher strains are expected there. On the other hand, Sensor 20 is the nearest FBG to the wing tip of the positive torsion side; therefore, the lower strains are expected there. Hence, both maximum and minimum measurement sensors performed in a suitable way during the flight tests and both critical cases produced reliable measurements. The strain datasets from all flight conditions were unfolded and then randomized in order to test the automatic damage detection methodology with the variations detailed in Section 4. Acquired data from flights with the pristine structure (BL_1, BL_2, BL_3, BL_4, BL_5 and BL_6) were concatenated and randomized, then 80% of these data were used for training and the

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remaining 20% was separated for testing (UND). Additionally, flight datasets having the same damage case were also concatenated and randomized so that the overall data is organized in eight matrices (BL, UND and D1 to D6). The filtering techniques detailed in Section 4 (i.e. KDE and Hampel identifier) were tested, having better results those that are based on data distribution instead of signal form. Standardization techniques described in Section 4 (i.e. global maximum strain, local maximum strain and average strain) were tested as well in different steps of the methodology: before clustering as conventional (step two) and after clustering. In this case, poor performance was achieved with the latter approach. In Table 3, a summary of the performance for the different tested filtering and standardization techniques (and combination of them) is presented in terms of F 1 score and accuracy for a 95% and 99% level of confidence. In conjunction with these performance metrics, the percentage of samples, defined as the ratio of samples after filtering to the original samples (765120) and the number of clusters identified by the clustering algorithm are included. Only those approaches giving suitable results in terms of F 1 score and accuracy are presented. Since each algorithm’s variation was tested ten times randomly choosing different parts of the dataset for training and testing, Table 3 reports mean values in conjunction with their corresponding standard deviation. The results showed that the OBS methodology is capable of considerably increasing the detection performance by achieving an accuracy of 0.955 for a confidence level of 95%, compared with an accuracy of 0.849 when no OBS was performed (same confidence level), implying a single cluster and a single PCA model containing all the operational conditions. These cases are the most single ones because no filtering and standardization were implemented. It is expected that the accuracy, when the OBS procedure is not performed, decrease even more whether the changes in the strain field promoted by a damage are smaller. In that case, such smaller changes would be more easily masked by the operational condition variations and, therefore, accuracy will be reduced. In overall, the filtering techniques are able to subtly improve the performance. However, the impact of this improvement should be considered carefully as argued by Marti-Puig et al. [92]. By using KDE filtering, rejecting data no meeting a confidence level of 95%, an accuracy of 0.981 and an F 1 score of 0.985 was achieved. If the confidence level is increased, which means rejecting or filtering less data, the accuracy and F 1 scores subtly decrease, as happened with KDE 99% and KDE 99.5%. On the other hand, by using Hampel identifier filtering, an accuracy of 0.972 and an F 1 score of 0.978 was obtained. Therefore, KDE filtering with 95% showed higher performance; however, the samples after this filtering was 77.19% of the original, compared with 90.76%, 92.58% and 94.78%, in the case of Hampel, KDE 99% and KDE 99.5%, respectively. This means that KDE 99% and the Hampel filter are more effective since demonstrated a similar performance without rejecting a large amount of data. On the whole, the use of a filtering technique for this application can be avoided because there is no considerable enhancement of the methodology and this additional step would complicate an online implementation for real-time damage detection implying an addition in the computational time. Standardization aiming data scaling and minimization of load magnitude effects did not work adequately in this application. Regardless of the technique used (i.e. global maximum, local maximum and mean standardization), the results were a decrease in the performance. Although in previous work with the aluminum beam subjected to dynamic loading and discrete pitch changes [53], standardization reduced the data variability and improve the methodology, in this composite wing structure under real flight, it produced the opposite effect. In this case, results having a greater number of clusters tend to have better metrics than those having less. Considering that standardization seeks for reducing variability, a smaller number of clusters were found. This means that the PCA models were built using a larger amount of data than when more clusters were produced. These models may be broad and general

Fig. 12. Data variability visualization using a factor analysis as dimensionality reduction technique. (a) Aluminum beam under dynamic loading and discrete operational variations [53]. (b) In-flight composite structure.

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showing high variability and containing several operational conditions, so that data coming from a damage case can fit the model and, therefore, can be misclassified as no damage. This effect was not produced in the aluminum beam experiment. These findings can be explained by analyzing data variability in the two cases. For this purpose, factor analysis was employed as a dimensionality reduction technique using a promax rotation and keeping three factors to allow data visualization in a three-dimensional space (since the datasets are multidimensional). The results for the aluminum beam are presented in Fig. 12a and for the in-flight composite structure case in Fig. 12b. It is demonstrated that easily identifiable clusters due to the changes in the pitch angle are produced for the aluminum beam case. On the other hand, the data acquired from the in-flight case demonstrated to have no clear transitions among clusters, therefore, data partitioning based on data density is more difficult. This can be explained by considering that in the real-world structures, discrete changes in the operational conditions are not expected and, additionally, the composite structure is stiffer than the aluminum beam so that changes in the strain field due to operational conditional are not as evident as in the aluminum structure. To summarize, the highest performances were achieved with the raw data, KDE with 95% and 99% confidence levels and Hampel filtering, all without standardization. The TPR, TNR, FNR and FPR for such cases are detailed in Fig. 13 and compared with the case of one cluster (no OBS performed) for a 95% (Fig. 13 and 99% (Fig. 13b) of confidence level. Regarding the confidence level, it can be appreciated that commonly higher performance was achieved by 95%. However, the analysis of deciding what level to use is non-trivial and depends on the application and aircraft operator. Comparing Fig. 13a and Fig. 13b, it can be seen that a 95% confidence level led to lower FNR than 99%; conversely, 99% confidence level led to lower FPR. This is explained since by increasing the damage threshold, the probability that damage cases slightly affecting the strain field fit the model is higher, producing more false negatives. On the other hand, by decreasing damage threshold, the probability that undamaged cases do not fit the model is higher and, consequently, more false positives may be present. This relationship is more evident in Fig. 14, where, for illustration purposes, damage index results (Hampel filtering with no standardization) of the clusters having the highest F 1 score and the lowest one are presented, as a way of showing two representative examples. This was carried out by counting the performance metrics for each cluster model and then calculating the individual F 1 score, cluster 69 (Fig. 14a) obtained the highest local score and cluster 88 (Fig. 14b) the lowest local score. Both scenarios are undesirable, however, depending on the structure operation it may be appropriated to select a determined confidence level. For instance, if the structure to monitor is an aircraft primary structural component, which in case of failures may cause catastrophic accidents, a more rigorous threshold is required (reject more data and lower the probability of a damage case to fit the model). Even though the system can be more prone to cause more false positives (false alarms) implying unnecessary downtime or maintenance checks, the safety is assured by reducing false negatives. Differently, if another component or other type of structure is considered, where the impact of a failure is not too critical or there is a considerable amount of time until a catastrophic failure occurs, a less rigorous threshold may be selected (reject less data and lower the probability of a pristine case to not fit the model). In the context of the filtering technique used, the highest performance results were achieved with KDE 99% filtering with damage indices calculated for a 95% confidence level, having a TPR, TNR, FNR, and FPR of 0.99, 0.965, 0.001 and 0.035, respectively. In contrast, the highest performance results obtained in the aluminum beam experiment were a TPR, TNR, FNR, and FPR of 1, 0.9824, 0, and 0.0128, respectively. It is a fact, that slightly higher performance results were achieved for the

Fig. 13. Performance analysis of the damage detection methodology using the most high-achieving proposed variations for a confidence level of: (a) 95% and (b) 99%.

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Fig. 14. Damage indices for PCA models with their associated damage thresholds for a 95% and 99% of confidence (dashed line and solid line, respectively). (a) Q index for cluster number 69 (best case). (b) Q index for cluster number 88 (worst case). BL corresponds to baseline (training data), UND to undamaged data (testing data) and D1 to D6 to damage cases.

aluminum beam; however, it is necessary to consider the structure of the data that involve the aircraft structure made of CFRP presented in this work, implying high variability, more different operational and environmental conditions and higher complexity. Although the final classification is not perfect, the methodology still presenting a high accuracy and high F 1 score, (0.981 and 0.978, respectively). Classification errors are mainly produced by outliers due to anomalous conditions such as unconventional maneuvers, abrupt gusts, sensing errors and the like. However, considering that these are only a small amount portion of the whole data, a decision tree for final damage classification can be used as suggested by the authors in [53]. A damage indication (alarm or report) will not be produced if the threshold is reached only in one measurement for a given instant of time, the indication is produced only in the case of constant measurements reach the threshold for a userdefined period of time. Thus, it is possible to enhance the performance of the methodology. Additionally, the performance of the methodology can be improved by adding updating capabilities to the OBS procedure. In the case of a new operational condition appears, which were not included in the initial training dataset, this will lead to consecutive data presenting no similarity so that the user can perform an update of the model to consider such new condition. 7. Conclusion A HUMS was developed and implemented in a fixed-wing UAV to acquire strain data in 20 different points of the wing’s front spar made of composite materials. The system was based on FBG sensors as source of information, seizing their advantages in terms of light weight, reliability, corrosion resistance, electromagnetic immunity and mainly their ability to be embedded in composite materials without being intrusive and deleterious for the aircraft performance. The HUMS is composed of a miniaturized data acquisition subsystem for gathering the strain measurements and a wireless data transmission subsystem as an enhancement for remote sensing and data management. The operation of the system was evaluated with a total of 16 flight tests, where it was possible to acquire in-flight strain measurements without data losses, sensor damages or system failures. The system demonstrated its capability to resist all the flight stages providing a reliable way of performing strain monitoring of real-world aircraft structures in operation. Data transmission from the aircraft to a ground station was accomplished over a WLAN using the IEEE 802.11 protocol. Strain data were successfully streamed during flight tests, showing signal stability and no data losses. The flight data were used to validate an automatic damage detection methodology based on machine learning, previously developed for a metallic structure under simulated operating conditions. This implies a total of 823413 samples (each sample contains 20 strain measurements) with around 35% of the data corresponding to strains acquired with the structure in a pristine condition and 65% to data acquired with the structure with one of the different six induced damage cases. The

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methodology was robust against environmental and operational conditions by using an OBS procedure and, then, performing final damage classification by means of PCA and their associated damage indices. Although some variations were tested to adjust the methodology to the real case (i.e. new filtering techniques based on data distribution, no standardization and the use of conventional PCA), it is shown that is possible to detect damages automatically with the methodology in a composite wing structure under flight testing with a suitable performance, an accuracy of 0.981 and an F 1 score of 0.978, for the case where KDE with a confidence level of 95% was used as preprocessing technique. The detection was achieved in all the flight phases and damages of different severities affecting the local stiffness of the structure can be detected. Thus, this system would allow to instantly know the occurrence of a damage with a high accuracy, aiming to diminish maintenance time and cost as it can help on efficient task planning. Its usage can improve companies’ perception alongside with operational safety and reliability. As it is possible to apply SHM to primary structural areas without removing components or carrying out tedious, human-based NDT techniques, maintenance times could be minimized reducing operative costs. This represents an advance towards closing the gaps for a broad implementation of SHM methodologies in real-world composite aerospace structures. Related work has been provided solutions for FOS-based strain monitoring of in-flight structures and some works have provided approaches to damage detection by processing the gathered information. However, the works involving damage detection from in-flight data are limited in terms of flight phase applicability, types of damages that can be detected and robustness against environmental and operational conditions. Future work will consist of coupling the damage detection methodology with the wireless data transmission system not only to provide a real-time strain monitoring as in this work, but also to provide real-time damage assessment. This would signify a challenge since the computational cost of the machine learning algorithms tends to be high, which may produce delays in the indications. Additionally, it is required to perform flight tests with different damage sizes, location and types in order to determine the sensitivity of the methodology. Finally, the development of new methodologies using novel techniques, including data fusion and different clustering approaches to improve the damage detection accuracy, reduce the computational time and overcome the problem of data partitioning due to no clear transitions among clusters will be also considered as future work. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors are thankful to the Centro de Investigación para el Desarrollo y la Innovación (CIDI) at Universidad Pontificia Bolivariana for funding this project with internal settlement number 636B-06/16–57. Additionally, special thanks to Ae. Eng. Alex López-Ríos from Aircomposites for his invaluable contributions to the UAV design and manufacturing. The authors sincerely appreciate the support given by MSc. Jorge Iván García-Sepúlveda and Ae.Eng. Juan Pablo Alvarado-Perilla, and the wise advice provided by Dr. Leonardo Betancur-Agudelo in the field of wireless data transmission and Dr. Ferney AmayaFernández in the field of photonic sensing. References [1] S. Rana, R. Fangueiro, Advanced Composite Materials for Aerospace Engineering, Woodhead Publishing, 2016. [2] F.-G. Yuan, Structural Health Monitoring (SHM) in Aerospace Structures, 1st ed.,., Woodhead Publishing, 2016. [3] P. Duchene, S. Chaki, A. Ayadi, P. Krawczak, A review of non-destructive techniques used for mechanical damage assessment in polymer composites, J. Mater. Sci. 53 (2018) 7915–7938. [4] A.N. Anoshkin, A.F. Sal’nikov, V.M. Osokin, A.A. Tretyakov, G.S. Luzin, N.N. Potrakhov, V.B. Bessonov, Non-destructive inspection of polymer composite products, J. Phys: Conf. Ser. 967 (2018) 012001. 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