Damage detection techniques for wind turbine blades: A review

Mechanical Systems and Signal Processing xxx (xxxx) xxx

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Review

Damage detection techniques for wind turbine blades: A review Ying Du a, Shengxi Zhou a,⇑, Xingjian Jing b, Yeping Peng c, Hongkun Wu d, Ngaiming Kwok d a

School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China d School of Mechanical and Manufacturing Engineering, The University of New South Wales, NSW 2052, Australia b c

a r t i c l e

i n f o

Article history: Received 30 April 2019 Received in revised form 9 October 2019 Accepted 16 October 2019 Available online xxxx Keywords: Wind turbine blades Nonlinearity Damage detection techniques Structural health monitoring

a b s t r a c t Blades play a vital role in wind turbine system performances. However, they are susceptible to damage arising from complex and irregular loading or even cause catastrophic collapse, and they are expensive to maintain. Defects or damages on wind turbine blades (WTBs) not only reduce the lifespan and power generation efficiency of the wind turbine, but also increase monitoring errors, safety risks and maintenance costs. Therefore, damage detection for WTBs is of great importance for failure avoidance, maintenance planning, and operation sustainability of wind turbines. This paper provides a comprehensive review of state-of-the-art damage detection techniques for WTBs, including most of those updated methods based on strain measurement, acoustic emission, ultrasound, vibration, thermography and machine vision. Firstly, typical damages of WTBs are comprehensively introduced. Secondly, detection principles, development methods, pros and cons of the aforementioned techniques for blade inspection, and their fault indicators are reviewed. Finally, potential research directions of WTB damage detection techniques are addressed via a comparative analysis, and conclusions are drawn. It is expected that this review will provide guidelines for practical WTB inspections, as well as research prospects for damage detection techniques. Ó 2019 Elsevier Ltd. All rights reserved.

Contents 1. 2. 3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Typical damages of WTBs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Damage detection techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Strain measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Acoustic emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Vibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Thermography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Machine vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fault indicators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Fault indicators in strain measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Fault indicators with acoustic emission characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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⇑ Corresponding author. E-mail address: [email protected] (S. Zhou). https://doi.org/10.1016/j.ymssp.2019.106445 0888-3270/Ó 2019 Elsevier Ltd. All rights reserved.

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5. 6. 7.

4.3. Fault indicators using ultrasound. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Fault indicators with vibration signals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Fault indicators based on thermography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Fault indicators in machine vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7. Summary of the fault indicators and the related feature extraction methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research prospects of WTB damage detection techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Declaration of Competing Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction Wind energy is one of the most important sustainable and renewable energy sources [1–3], which can be converted into electrical energy for industrial power supplies and household electricity consumptions [4]. In 2012, wind energy amounts to 11% of the total renewable energy generation, and is an indication of national competitiveness [5]. With the increase of global wind energy capacity installation to 539 GW in 2017 [6] and the trend of increasing WTB sizes [7,8], stringent challenges are imposed on their damage detection [9] and structural health monitoring (SHM) [10], where 19.4% of all the wind turbine incidents in 2012 are attributable to blade failures [11,12]. Blades are the key and crucial components of a complete wind turbine power generation system operating in rough conditions, which transfer wind power into electrical energy [13,14]. They have significant effects on the overall performance of the wind turbine and are costly in manufacture (15–20% of the total cost) and maintenance compared with other components [12,15,16]. Composite materials, such as fiber reinforced polymer (FRP) composites that are frequently used to fabricate a WTB [16], have been typically used in WTBs to reduce the general cost and weight, and to improve the strength, stiffness, damage resistance, fault tolerance and service life [17]. Defects or damages on WTBs caused by production defects, turbulent wind, lightning, irregular loading, and so on [13], may lead to surface changes that influence blade aerodynamics efficiency [17,18]. It would damage the wind turbine itself or adjacent ones, and even impose safety hazard to human operators [9]. This may result in power loss, high maintenance costs [19,20] and so on. Wind farms are mostly installed in remote areas, such as onshore areas, mountainous areas, offshore areas, desert areas and others, and the blades are often located at high distances to ground. Turbine blades that are working under complex natural environments with stormy winds, salty fog, rain, and so on, are exposed to different kinds of damages and challenges. Therefore, it is not easy to detect the damages and provide maintenance strategies of the blade, which will result in long-term shutdown, power generation loss, blade re-lifting and replacement, and high economic loss, and others. Moreover, the percentage of the operation and maintenance costs of offshore wind turbines is 15–35% [21], which is higher than that of onshore wind turbines. Therefore, it is of great significance to detect WTB damages at the earliest possible stage to avoid blade failures, to better operate the turbines [9,22,14], and to satisfy requirements of safety, durability and sustainability [23]. This requires implementing health-based maintenance management, labor cost reduction, downtime minimization, prevents unnecessary replacement [24], and improves wind energy harvesting and so on. A large amount of damage detection technologies, mainly based on strain measurement, acoustic emission, ultrasound, vibration, thermography, machine vision and so on, have been employed for the inspection of WTBs. Moreover, acoustic emission, strain measurement, ultrasound, and machine vision-based inspection technologies have great application potentials for on-line monitoring [9,25]. There are several previous academic researches focusing on the conventional and newly developed sensors for damage detection, and fault diagnosis approaches. Ochieng et al. [10] reviewed the use and study of ground-based radar as the transducer for WTB monitoring. Li et al. [9] presented the developments of several types of sensors and damage detection methods for WTBs in order to avoid blade failure and increase their reliability. Raišutis et al. [26] and Amenabar et al. [27] reported the strengths and weaknesses of Non-destructive Testing (NDT) for WTB inspection. Previous works by Zhou et al. [28] and Yang et al. [29] presented the structural testing of WTBs, and the failure mechanisms of the blade was revealed [28]. Schubel et al. [30] reviewed the SHM methods for WTBs, including acoustic emission, strain measurement, ultrasound, and thermal monitoring. Tchakoua et al. [31] demonstrated wind turbine monitoring technologies, and the trends, challenges and applications were discussed. To the best of our knowledge, although there are some reviews having been reported for WTB damage detection techniques, the discussion on fault indicators and research prospects of damage detection for WTBs are still not given. Moreover, it is also meaningful to present the progress and trends of damage detection technique developments for WTBs. The rest of this paper is organized as follows. In Section 2, typical defects or damages of WTBs in operation are introduced. In Section 3, detection techniques for damage inspection based on strain measurement, acoustic emission, ultrasound, vibration, thermography and machine vision, are reviewed. Furthermore, detection principles, development methods, pros and cons of each technique are investigated. In Section 4, fault indicators for each aforementioned detection method are summarized. In Section 5, discussions on damage detection techniques for WTBs are described through a comparative analysis. Possible research directions of WTB inspection are discussed in Section 6. Finally, conclusions are given in Section 7.

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2. Typical damages of WTBs Damages to a wind turbine blade can be resulted from a variety of causes during continuous operation [7,9,11,32], such as wind, heavy rainfall, lightning strike, ice accumulation [12], insufficient blade material strength, fatigue loads, personal errors during manufacturing, installation and so on. Damages reduce power production caused by aerodynamic efficiency loss [32–36], and decrease the lifespan [37]. Meanwhile, they also increase the noise generated from blade surface irregularities [38,39], and increase monitoring errors [40,41], safety risks [36,42,43], and others. The occurrence of WTB damages caused by extreme environmental factors, such as thunderstorms, heavy rainfalls and strong winds accounts for 76% of the total damages [11]. In detail, lightning strikes will cause damages especially at the outmost part of the turbine blade, such as delamination, debonding, shell and tip detachment [44]; strong wind will cause blade breakage or failure; ice accumulation will cause unbalanced rotation [36], aerodynamics loss, unwanted stop, and increase blade fatigue and safety risks [45]. Turbine blades operating in extremely cold weather may suffer from material brittle fracture and icing of blades and sensors [45]. Therefore, the earlier the damages are identified, the lower the maintenance costs are expected to be. Typical damages that appear on the turbine blades [7,9] include cracks (increasing surface roughness [46]), skin/adhesive debonding and buckling induced skin/adhesive debonding (losing the ability of materials to adhere to each other [25]), adhesive joint failure between skins, delamination (reducing blade stiffness [46]), fiber breakage, interlayer peeling, edge erosion [47] and so on. It can be seen that cracks, delamination and debonding are common types of damages [9,48]. With fatigueinduced degradation on turbine blades, cracks appear in the blade body, and then propagate rapidly, or even collapse the entire structure [9]. Typical damages of WTBs are listed in Table 1 and illustrated in Fig. 1. Damages are often distributed in different regions of the blade, but the root section of the blade and the bonded/welded joint are the most likely damaged locations [25,44].

3. Damage detection techniques The purpose of damage detection is to ensure that the damage can be monitored before it causes catastrophic incidents or disasters to the wind turbine. It is applied to make sure that the blade is operating in a good condition and is still fulfilling its functions. Moreover, it is employed to identify the root cause of the damage and thereby promote effective maintenance [50]. Lightning detectors are usually equipped on the wind turbine in order to directly prevent it when the blade is subjected to lightning strike [50,51]. Damage detection techniques [26,27] with respect to the structural monitoring or material changes of WTBs are mainly based on strain measurement, acoustic emission, ultrasound, vibration, thermography, machine vision and others. Especially, detection techniques based on strain measurement, acoustic emission, ultrasound and machine vision are potential and promising technologies for on-line implementation [9,25]. Although these detection techniques had been widely developed and utilized, there was no single optimal technique available. It was preferred to integrate several techniques to detect more comprehensive WTB defects [26]. The rest of this section will present the detection principles, development methods, pros and cons of damage detection techniques based on strain measurement, acoustic emission, ultrasound, vibration, thermography, and machine vision.

3.1. Strain measurement Detection methods based on strain measurement are applied to detect minute changes in length or deformations of the turbine blade by using strain sensors [30]. Blade structures deform under the applied loads, and the deformation can be obtained by detecting the strain. Direct strain and shear strain are defined and utilized in WTB inspection. The direct strain is defined as e ¼ x=l, and the shear strain is defined as c ¼ x=d, where the definitions of the two types of the strain are illustrated in Fig. 2 [30]. Strain sensors are often installed on the surface or embedded in the layers of the blade [46], which can indirectly detect structural damages in WTBs through the expansion or contraction of the blade caused by temperature or strain variation [52]. Additionally, strain measurement has the advantage that it can continuously monitor the turbine blade for long periods of time [53], but the accuracy and sensibility of strain measurement are dependent on the distance between the location of the sensor and the damage.

Table 1 Typical damage types for WTBs [7,9,49]. No.

Damage types for WTBs

Internal/Outer

1 2 3 4 5 6 7

Skin/adhesive or main spar/adhesive layer debonding Adhesive joint failure between skins Sandwich panel face/core debonding Delamination driven by a tensional or a buckling load Fiber failure in tension, laminate failure in compression Skin/adhesive debonding induced by buckling Cracks or debonding of the gel-coat

Outer Outer Outer Internal Internal/Outer Outer Outer

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Fig. 1. Sketch illustrating some typical damage types of WTBs [49].

Fig. 2. Definitions of the two types of strain [30].

Strain gauges, as shown in Fig. 3, were often used to monitor the blade strain to prevent high stress and detect damages [15]. It is possible to estimate the blade lifespan by using the strain measurement at specified positions [31,54] and make decisions to shut down for maintenance or replacement [25]. However, strain gauges are easy to fail during long-term operation because of disbanding, fatigue or creep [54]. Fiber Bragg Gratings (FBGs), whose principle is illustrated in Fig. 4, are commonly applied to optical fibers with periodic refractive indices and can monitor strain and temperature simultaneously [52,55]. Bragg wavelength, kB , is employed to directly reflect the strain and can be computed by Eq. (1) [52,55,56], and the variation of the Bragg wavelength, DkB , can be expressed by Eq. (2) [52].

kB ¼ 2  neff K

ð1Þ

DkB ¼ kB fða þ nÞDT þ ð1  pe ÞDeg

ð2Þ

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Fig. 3. Strain gauges [30].

Fig. 4. Fiber Bragg Grating [56,57].

where neff is the effective refraction index; K is the grating period; a is the thermal expansion coefficient; n is the coefficient of thermo-optic; DT is the variation of temperature; pe is the strain optical coefficient; De is the variation of strain. Compared with FBGs, strain gauges do not show noticeable advantages that FBGs can be freely and directly embedded into the materials, and engraved into one single optical fiber. On the other hand, strain gauges need to be mounted on the WTB surface and at least two wires are required [58]. FBGs are proved to be more suitable for strain measurement in WTBs than strain gauges [7,57], and are possible to be embedded into blade structures during the manufacturing process [58]. Although optical fiber sensors possess high sensitivity, small size, light weight, high fatigue resistance, wide range of operating temperature, and are possible to be embedded in composites, and so on [58], their application potential being demonstrated in WTBs monitoring is limited because of its higher cost [9,54]. Sierra-Pérez et al. [58] proposed a novel methodology based on strain measurement in real time to detect damages in WTBs, and pattern recognition methods were employed to detect the defects and nonlinearities for the certification testing of WTBs. Wu et al. [59] presented a novel strain sensor for WTB measurements with possible applications for thin plate structures and shells, which could generate a 2-dimensional strain map and deflection shapes of the surface for on-line state assessment. Tian et al. [60] proposed a feature information fusion method to fuse the information of Chi-square distribution from FBGs to detect the damages on WTBs, and the feasibility of this proposed method has been verified with a strain sensor system. Laflamme et al. [61] formulated a strategy for damage detection composing of soft elastomeric capacitor (SEC) with low costs to measure surface strains, which shown the possible potential of localization, damage detection and prognosis for WTBs. Lee et al. [62] proposed a monitoring system for deflection detection of WTBs with strain sensors and an algorithm based on the correlation between the deflection and the strain. The authors also proposed a monitoring system without wires to monitor vibration responses of an in-service blade. Aihara et al. [63] developed an on-line monitoring system for WTBs by attaching a few strain gauges at the root of a blade to estimate the deflection with the measured strain, and the sensing results were obtained by a wireless connection. Schroeder et al. [64] implemented continuous load monitoring of WTBs in operation by using optical FBGs, and indicated that the results can provide guidance for blade development in the future. Ramakrishnan et al. [56] reviewed fiber optic sensors for strain measurements in composite materials such as WTBs. The authors indicated that to monitor the strain and temperature of the structure simultaneously was favorable, to provide reliable connection methods to sensors was a challenging issue, and to embed large numbers of sensors will bring

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intensive labors. Fiber optic sensors are promising for continuous strain measurement especially for WTBs, but are still too expensive, time-consuming on installation and require prior knowledge on high stress field. Detection technique based on strain measurement is available for continuous monitoring of rotating blades, and researches are prone to low cost, high accuracy, wireless, approaches on pattern recognition and others. Advantages and disadvantages of the strain measurement-based detection technology are summarized in Table 2. 3.2. Acoustic emission Detection methods based on acoustic emission focus on the detection of electrical signals converted from transient elastic waves caused by the release of energy from damage initiation, crack propagation or plastic deformation [65]. The principle of acoustic emission detection is presented in Fig. 5. A damage will give rise to the burst of energy generating sound waves with high frequency within the blade structure, and can be monitored by acoustic emission sensors [65]. The occurrence, propagation process, and the failure of the blade can be obtained through acoustic emission waveform characteristics, such as amplitude, energy, rise time, root mean square (RMS) value, and so on [25,31,66]. This technology monitors micro-structural changes in the materials of the WTB arising from fatigue, crack, reduced stiffness, and increased surface roughness [46] and others without any external excitation. By analyzing acoustic emission data, the damage criticalities can be estimated [15]. It is increasingly popular for material or structure monitoring from defect to failure, and is proved to be an effective detection method to monitor damage expansion [24], and to assess the time of damage occurrence, location and the severity of the damage in WTBs during operation [67]. The technique has the advantages of being rapid, efficient, non-invasive, and detecting damages much earlier than the vibration-based technology [15,68,69], and it is highly related to the vibration-based technique [54]. Technology based on acoustic emission was usually employed in critical areas and WTB surface interfaces that defects or damages often occurred [68], to identify failures and locations of damages in the turbine blade. The installation and application of the acoustic emission-based technique are shown in Fig. 6. The acoustic emission sensors have to be mounted on the blade, and repetitively monitor the blade health under periodical high loading. Tsopelas et al. [71] reviewed the application of acoustic emission measurement techniques on structural integrity assessment of WTBs from laboratorial static tests to long-term full-scale monitoring during operation over the last two decades. Jüngert [72] improved the inspection of WTBs by using acoustic waves. Han et al. [67] applied a new source location method with energy-based contour mapping to assess damages of a full-scale experiment-designed blade under static loads. Tang et al.

Table 2 Advantages and disadvantages of the strain measurement-based detection technology [25,46,53,54]. No.

Advantages

Disadvantages

1

Detection of minute structural changes in the blade, and efficiency for incipient fault detection Lower sampling rates requirement External power sources free No restriction on time and transmitting distance with respect to signal degradation Available for continuous monitoring and lifetime prediction in operation

Accurate results are subject to the contact of strain sensors and the monitored materials Increase of system complexity Prior knowledge about high strain areas Prone to failure due to creep, disbanding or fatigue and rely deeply on sensor reliability Requires large amounts of sensors because one sensor measures only at one point

2 3 4 5

Fig. 5. Principle of acoustic emission detection [9,65].

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Fig. 6. Acoustic emission-based detection of a stationary WTB in service (reproduced from [70]).

[73] applied the acoustic emission-based technique to monitor the WTB structural health, and successfully detected the increase in fatigue damage and determined the location of damage growth. Zarouchas et al. [74] used the acoustic emission technique to monitor the damage process and achieve a differentiation of damage mechanisms. Bouzid et al. [75] integrated the acoustic emission-based technique, the wireless technology, and proposed an in situ structural health monitoring system for WTBs. Van Dam et al. [76] provided a guideline for acoustic emission research with application to SHM of WTBs for on-line purpose. Detection technology based on acoustic emission not only monitor the structural health of WTBs, but also assess the damage severity by analyzing acoustic emission signals. With acoustic emission data, Tang et al. [77] presented a pattern recognition methodology to classify different damage mechanisms for a long WTB from the fatigue test. Gómez Muñoz et al. [78] developed a novel signal processing approach to detect the fiber breakage in a WTB, and the defect location can be accurately obtained. Zhou et al. [79] monitored the damage and failure process of the delaminated composites for WTBs, discussed the shear failure mechanisms of the materials, and verified the origin and propagation of the damage by using acoustic emission signals. Bo et al. [80] employed the blind deconvolution separation method to find the correlation between fatigue status and acoustic emission signals of a material with a blind deconvolution separation approach from a WTB fatigue test, where the crack was artificial and transverse. Using Lamb waves with piezoelectric transducers installed on the root of the turbine blade, Li et al. [81] achieved transverse crack detection, where sparse reconstruction was employed to detect the location and the sparse pursuit algorithm was adopted to obtain the extension of the artificial transverse crack. Additionally, Li et al. [82] also proposed a sensor array sparse optimization of the composite laminate structures to decrease the transducer numbers, and the accuracy was still guaranteed. However, a large number of acoustic emission sensors are required to be installed on a WTB in order that the sensor can be placed near the location of blade damages [9], and there is no physical connection between the acoustic emission signal and the related damage [25]. It demands a data acquisition system with high sampling frequencies, and distinguishing signals from acoustic emissions and noisy environments is very difficult, which makes the data processing task complicated and much more expensive [25,68]. Moreover, the technique cannot provide information about the internal structural stress of WTBs [25,30]. Generally, the detection system of the WTB inspection based on acoustic emission is complicated and the cost is increased undoubtedly. Advantages and disadvantages of the acoustic emission-based detection technology are shown in Table 3. The main challenge for this technique is to distinguish the acoustic emission signal generated by the damage from that caused by the noise. 3.3. Ultrasound Detection methods based on ultrasound detect reflected waves from the damage when the ultrasonic waves are transmitted through the material and received on opposite surfaces [44,31], as shown in Fig. 7. The ultrasonic technology is one of the most widely used NDT techniques in industry [86]. The method is dependent on the propagation and reflection of elastic waves within the blade [84]. Specific reflection, attenuation, resonance and transmission patterns can be obtained depending on the differences of the material or structure [27], therefore, the size, location and other information of the damage can be evaluated through these patterns. The ultrasonic technology was used extensively for investigating inner structure damage (e.g., delamination, debonding, and so on) [15,68]. It can detect damages with a few millimeters in length [44]. The transmission time indicates the damage position and the amplitude assesses the severity of the damage [30,87]. In-depth information of the damage can be also obtained by using the ultrasonic technology, but its success was limited by complications in signal processing, prolonged acquisition time and the need to contact with the surface [27]. These problems limit its application potential for the inspection of WTBs or other large components. Please cite this article as: Y. Du, S. Zhou, X. Jing et al., Damage detection techniques for wind turbine blades: A review, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2019.106445

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Table 3 Advantages and disadvantages of the acoustic emission-based detection technology [9,25,30,31,44,66,68,83–85]. No.

Advantages

Disadvantages

1 2 3 4 5

Higher frequencies from 50 kHz to 1 MHz Highly sensitive to different types of damages Available for continuous monitoring and early detection Possibility for damage visualization and localization Relatively high signal-to-noise ratio

Have to be mounted on the blade Requires a large number of sensors Complicated data processing due rightarrow high frequencies Increased system complexity and high cost Inevitable signal attenuation

Fig. 7. Principal and inspection areas of the ultrasound-based technique for WTBs (a) the inspection area [7], (b) principal of the pulse-echo technique [44,84,88] (GFRP, Glass Fiber Reinforced Plastic).

Ultrasonic Guided Waves (UGW) are ultrasonic elastic waves used to detect structural damages [89,90], including the location, the type and the severity of the damage [25]. This is one of the most efficient and reliable techniques [33]. Park et al. [91] proposed a technique through non-contact laser ultrasonic scanning by using a standing wave filter to detect the hidden delamination and debonding in composite structures. Ye et al. [86] developed an automated non-destructive testing system using the pulse-echo ultrasound for in situ inspection of WTB internal damages. Habibi et al. [92] combined UGWs and low-frequency vibrations for ice removal on the composite blade surface. Yin et al. [93] proposed an ultrasonic de-icing system for WTBs, which was proved to be feasible for de-icing purposes. Park et al. [94] formulated a scanning strategy for a non-contact laser ultrasonic measurement system to improve the performance of the WTB inspection, where coarse scanning and dense scanning were performed with a low/high spatial resolution in different scales of the tested blade. Zuo et al. [95] presented a novel damage identification algorithm using UGWs based on 2-dimensional multiple signal classification, which can be applied to recognize the turbine blade damages. Shoja et al. [96] applied the guided wave for ice detection of WTBs operating in cold climate regions, with numerical simulations and experimental validations. Jiménez et al. [97] employed signal processing (wavelet transform) and machine learning (pattern recognition) with guided waves to detect and diagnose the levels of dirt and mud on WTBs. Moreover, Jiménez et al. [33] presented machine learning classifiers on ultrasonic signals to identify distinct levels of ice accumulation in WTBs. The ultrasonic technology has been proved to be popular and efficient for blade de-icing. With the advantages of high speed of scan, good resolution, and flaw detecting capabilities, the location, orientation, size and other features of the damage can be achieved [66]. Advantages and disadvantages of the ultrasound-based detection technology are listed in Table 4.

3.4. Vibration Detection methods based on vibration focus on the monitoring of abnormal vibration caused by irregular oscillation, deformation occurrence and so on, and are concerned with vibration signals representing dynamic properties of WTBs, such Please cite this article as: Y. Du, S. Zhou, X. Jing et al., Damage detection techniques for wind turbine blades: A review, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2019.106445

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Table 4 Advantages and disadvantages of the ultrasound-based detection technology [25,27,28,44,66,91,98]. No.

Advantages

Disadvantages

1 2

Extensively for inner structure damage (e.g., delamination, debonding, and so on) Available for position, in-depth and severity of the damage

3

Applicable for small defects detection

For lamination, the resolution is too low to obtain the accurate position Requires surface contact and long acquisition time (timeconsuming) Signal processing is complicated

as frequency-response and modal parameters of the blade structure [99], to identify damages causing changes in these properties [9]. The vibration-based technique with the mechanics characteristics of the structure itself, such as natural frequencies, modal shapes and damping, and so on, is not convenient for online detection because of its complexity and time-consuming. However, the vibration-based technique using structural response signals from on-line measurement can implement online and continuous damage detection with intelligent methods to insure the reliable operation of WTBs [99]. It has been widely applied on rotational machinery monitoring [12,54,100]. The installation of vibration sensors is usually on the surfaces of WTBs [68]. Displacement sensors, velocity sensors and accelerometers are the major types of sensors to capture vibration signals. The frequency ranges from low-frequency, middle-frequency and high-frequency ranges, and are used in the corresponding vibration sensors, respectively [15,26,31,54,68]. Due to the wide range of frequency, accelerometers are widely used for WTB inspection. Signal processing techniques, such as frequency analysis, time analysis, and time-frequency analysis, are especially vital to obtain different signatures to detect WTB damages [12,101–103]. Wavelet transform [104], empirical mode decomposition (EMD) [105–107], full Fourier transform, support vector machines (SVM) [108–110], hidden Markov models (HMM) [111,112], deep neural networks (DNN) [113] and other classifier-based methods [114] and feature extraction methods [103] are regarded as the signal processing tool for blade fault detection. Recently, in order to improve the efficiency, on-line feasibility, and immediacy for the WTB damage detection, researchers mostly focus on signal processing techniques and algorithms [25,103]. However, the distinction of the acquired signals from damaged blades and environmental and operating conditions is still a big challenge [25]. Ghoshal et al. [22] tested four vibration-based detection technologies to detect damages on a fiberglass blade using piezoceramic actuator patches. Wang et al. [115] proposed a method for damage detection of WTBs by integrating the finite element method (FEM) and the mode shape difference curvature information, where the location and the severity of the WTB damage can be determined. Dervilis et al. [116] utilized vibration response data to a pattern recognizer on a 9 m CX-100 blade to detect the blade damage. Skrimpas et al. [117] employed lateral vibration data from the nacelle and power performances reduction to detect ice accretion, and 13 wind turbines were employed to validate the efficiency of the proposed approach. Ulriksen et al. [118] studied a damage identification method on structural mode shapes with modal and wavelet analysis to detect and localize a 1.2 m trailing edge debonding on a 34 m blade. Oliveira et al. [119] introduced a vibrationbased monitoring system with the modal properties, which were capable of monitoring blade damages in both onshore and offshore wind turbines. Dolin´ski et al. [120] employed FEM and the laser scanning vibrometry to determine the size and location of delamination in WTBs. Hoell et al. [121] proposed an optimized data-driven vibration-based method using multivariate damage sensitive features extracted from acceleration responses for the damage detection. Zhang et al. [122] used a random forest classifier to detect turbine blade icing, by analyzing the data from a wind farm with combined vibration signals in a supervisory control and data acquisition system. Colone et al. [123] proposed a methodology, which only relies on signal frequencies to detect the mass changes for WTBs. And the approach of statistical pattern recognition was adopted. Hoell et al. [124] monitored the structural health of WTBs by regarding autoregressive model coefficients as the damage features to improve the ability of early damage detection. Ground-based radar (GBR) is being used as a vibration-based non-contact sensor for SHM of in-field WTBs, which is suitable for remote vibration detection of the tested object [10]. Summers et al. [125] and Talbot et al. [126] established a noncontact measurement method using the coherent laser radar technology for the large-scale offshore WTB inspection, and provided a proposal for the inspection of other large, non-rigid structures. Arnold et al. [127] reported a radar-based approach with a low localization error for the SHM of WTBs with the development of a bistatic frequency-modulated continuous wave radar operating from 33.4 to 36 GHz. Moll et al. [128] introduced a radar-based new SHM approach for remote and in-service WTB inspection using a frequency range of microwaves and millimeter-waves. Advantages and disadvantages of the vibration-based detection technology are summarized in Table 5. Environmental and operational conditions, such as wind speed, the rotational speed of WTBs, temperature, ambient loading conditions and so on, may obviously influence the dynamic properties of the blade, such as natural frequency, mode shape and damping [129–131]. The difficulty and the research hotspot of the technique is to distinguish the vibration caused by damages from the vibration caused by environmental and operational conditions. 3.5. Thermography Detection methods based on thermography aim at the detection of variations in thermodynamic properties of the blade, which allow for scanning large WTB surfaces [29,87]. Material damages of the blade can be measured by temperature graPlease cite this article as: Y. Du, S. Zhou, X. Jing et al., Damage detection techniques for wind turbine blades: A review, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2019.106445

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Table 5 Advantages and disadvantages of the vibration-based detection technology [25,132]. No.

Advantages

Disadvantages

1 2 3

Non-destructive and high sensitivity Prone to implement Available for location and severity of the damage

Unable to monitor early damages Unable to observe the damage directly Affected by the environment and may make errors on damage detection

dients non-destructively, and the detected part with damages has higher temperature than the normal part [31]. Infraredbased sensors or cameras are usually used to visualize variations in turbine blade surface temperature [15,133]. However, it is not applicable for fault detection at an early stage due to the slow temperature development [31]. A scheme of the thermography technique using infrared-based camera as well as the thermogram results is shown in Fig. 8. The result consists of five stitched partial thermograms, and the differences in temperature indicate the potential defects on the subsurface of WTBs. The temperature differences near the hub represent the potential subsurface damages of the WTB [134]. Yang et al. [133] concluded the current status and the use of infrared photography techniques to detect damages and assess the health of WTBs, and indicated that this kind of techniques had not been widely used in industry. Galleguillos et al. [135] employed the infrared thermography (IRT) and used unmanned aerial systems (UAS), as a non-destructive technique, to detect in-service damages in a composite WTB. Muñoz et al. [136] proposed a novel approach using thermal infrared radiometry for blade icing detection without any physical contact. Doroshtnasir et al. [134] employed thermography to detect potential subsurface defects or damages in offshore wind farms. The operation can be performed from remote distances by a data processing algorithm that is different from the common thermographic analysis method on photographical thermographic images. Hwang et al. proposed a continuous line laser thermography technique [137] and a continuous-wave line laser thermography system [138] for WTB monitoring without destruction. The system functions under rotating conditions by generating thermal waves and records the corresponding wave propagation with an infrared camera. Sanati et al. [139] investigated two kinds of the thermography, including a passive and active pulsed thermography, and a step heating and cooling thermography. It is able to monitor WTB defects, where image processing plays a vital role on the accurate detection of internal defects. However, it is difficult but important to highlight the influence from the blade damages on temperature and eliminate the effects from other factors. Advantages and disadvantages of the thermography-based detection technology are given in Table 6.

Fig. 8. Scheme of the thermography technique and the thermogram results [134].

Table 6 Advantages and disadvantages of thermography-based detection technology [25,27,29,31,44,140,141]. No.

Advantages

Disadvantages

1 2 3 4

Available for full-field measurement Suitable for detecting damages caused by fatigue and sensitive to delamination Available to interpret visually Short inspection interval

Limited for on-line monitoring Affected by temperature or air humidity Inapplicable for early fault detection Requires thermal image processing

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3.6. Machine vision Detection methods based on machine vision use sequences of 2-dimensional/3-dimensional images from different locations and perspectives to obtain the information of the target object, whose principle is similar to the stereoscopic view of human vision [28]. Recently, with the great development of computer science and optics devices, the machine vision-based approach has been applied frequently for SHM in damage detection, dynamic identification and so on [142,143]. This method can improve the detection accuracy and efficiency, which is determined by image processing algorithms. In addition, machine vision-based methods are less affected by the environmental factors than other detection methods. Moreover, this method has been proved to be an efficient and cost-effective method in monitoring turbine blades during operation, and is the only way to detect low pressure surface buckling [144]. The machine vision-based detection technology is able to detect damages that are visible on the surface (external damages), such as cracks, scratches, and so on. The prototype of the machine vision-based detection technology is shown in Fig. 9. The illustration shows the binocular vision detection method, which acquires the blade information by obtaining images from two different positions based on parallax principles. The detection system composes of image acquisition, image processing and damage identification [143]. This technology imposes high requirements on imaging devices and data processing capabilities, especially for the purpose of on-line monitoring [25]. The technology can also reduce the exposure of human workers to dangerous tasks in the wind farm [145]. Johnson et al. [144] constructed a stereo-videogrammetry system to monitor the airfoil shape and the surface motion of the turbine blade. Yang et al. [147] developed a videometric technique to detect blade deformations and understand the structural behavior of WTBs in the large-scale range during operation. Wu et al. [148] discussed the vision-based approach to detect large-scale structures such as turbine blades, and the structural displacement can be extracted by recording and analyzing image sequences with image processing techniques, such as edge detection algorithms, matching algorithms, and others. Akhloufi et al. [141] presented a computer vision-based method to measure ice accumulation on WTBs in operation, where a digital camera was employed and algorithms on image processing were developed. Poozesh et al. [149] used 3-dimensional digital image correlation (3D DIC) to capture full-field strain over large areas of a WTB, where a pair of stereo cameras were utilized to obtain the surface geometry, deformation and strain on the blade surface. With the research foundation, Poozesh et al. [150] also proposed a multi-camera measurement system with conventional 3D DIC and 3-dimensional point tracking approaches to measure the entire surface of WTBs. It is essential to automatically detect damages for WTB maintenance. Stokkeland et al. [151] presented an autonomous machine vision approach using an unmanned aerial vehicle (UAV) for recognition and tracking a wind turbine, as well as the blades, and Hough line transform was used as the recognition algorithm and a Kalman filter was applied for tracking. Wang et al. [152] proposed a data-driven automatic crack detection framework for WTBs based on UAV-taken images by using Haar-like features. Babu et al. [153] described a crack recognition method based on textual features to obtain automated inspection for early cracks in WTBs. Moreno et al. [154] introduced a deep learning vision-based approach using a camera mounted on a robotic system to automatically monitor

Fig. 9. Prototype of the machine vision-based detection technology by referring to [146,147].

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Table 7 Advantages and disadvantages of the machine vision-based detection technology [16,25,144,145,152,159]. No.

Advantages

Disadvantages

1 2 3 4

Low cost Suitable for surface damages Evitable for safety risks of human laboring Available for displacement of the damage and on-line monitoring

Requires heavy computation Not applicable for standalone use Accuracy relies on image processing Not applicable for explanation on physical mechanism of damages

each part of the blade surface. Damages, such as impact of sun rays, wear and fractures, can be detected. Vision-based approaches have been commonly used with other monitoring techniques [25]. Daud et al. [155] employed visual nondestructive health monitoring and the piezoelectric sensor to assess the damage of the blade caused by lightning strikes. Yang et al. [156] introduced photothermal thermal-wave-radar non-destructive imaging to inductive infrared thermography-based machine vision, and applied thermal-wave-radar to the imaging inspection and diagnosis of WTBs. Researches on machine vision-based approach for SHM of WTBs are still at an early stage, especially in complex backgrounds. However, with the advances and achievements in machine vision, it is a promising technology on surface detection of high altitude or large equipment/buildings in the future and the significant reduction of installation costs [143,157], fullfield measurement [150,158], intuitive and temperature-free information for structure inspection can be provided [159]. It is indicated that the detection accuracy can be easily increased by applying a specialized hardware or data processing methodologies [142], and it is vitally important to solve the difficulty of obtaining high-definition and multi-view images of WTBs and extracting damage information from complex backgrounds. Therefore, future researches should focus on the research of image processing technologies and related algorithms, simultaneous localization and mapping, machine learning for the damage recognition, and others. Advantages and disadvantages of the machine vision-based detection technology are shown in Table 7. 4. Fault indicators A typical condition monitoring and fault diagnosis system for wind turbine blades is illustrated in Fig. 10. Condition monitoring using the above-mentioned detection techniques based on strain measurement, acoustic emission, ultrasound, vibration, thermography, machine vision and so on, is the first step to detect blade damages. Then, signals will be acquired from detection methods and sensitive features will be extracted, thus transform into fault indicators to implement fault feature extraction and fault diagnosis of WTBs afterwards [68]. Sometimes, fault indicators are derived from signals in the detection monitoring process and by means of various analysis methods to extract damage-sensitive but environmentally insensitive features [160,161] to implement the blade health diagnosis and remaining lifetime estimation. The key issue is how to define the fault indicator and how to use the indicator to diagnose the turbine blade. Sohn [129] indicated that the fault indicators that are sensitive to damages are often sensitive to abnormal operations and environmental variations. Ideally, when fault indicators increase to the pre-determined threshold, alarms should be taken. However, exceeding a fault criterion does not necessarily result in failure of the whole structure, because failure of the blade is determined by several fault indicators [162]. This section presents a summary of fault indicators of the above-mentioned detection techniques, and discusses their potential advantages and disadvantages in application. 4.1. Fault indicators in strain measurement Strain signals, such as strain, peak strain, strain rate, Bragg wavelength deflection, and so on, can be employed to judge the structural damages or failures, and estimate the damage sizes [57,62], where even small structural changes can be detected. The peak strain of the blade structure, can be used to effectively identify damage locations in the blade structure [54]. The strain rate of the blade structure can be used to measure crack initiation caused by higher strain loads, and to prognose the severity of the structural failure [54], thus provide maintenance strategies for the turbine blade. As the strain of the structure becomes higher with the increasing loads, analysis methods, such as Chi-square distribution [60], statistical tools [58], experimental modal analysis [62,63], feature information fusion [60], and so on, are employed to

Fig. 10. Typical condition monitoring and fault diagnosis system for WTBs.

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determine the blade damage. Tian et al. [60] adopted the Chi-square distribution based on the strain responses as the sensitive feature to display the structural dissimilarity arising from damages, and a feature information fusion method was employed to perform damage detection for WTBs. Sierra-Pérez et al. [58] employed nonlinear Principal Component Analysis (NLPCA) model to reduce nonlinear dimensionality of the strain field in blade structures, and T 2 index and Q index had been regarded as common tools for anomaly detection. Detection methods based on strain measurement require many sensors, thus the measured indicators (strain) can provide more information about the blade structure. However, the drawback of this method is that the requirement implies much more measurement data and computational time [58]. Moreover, as the sensors are prone to failure, it may make determined results unreliable. 4.2. Fault indicators with acoustic emission characteristics Tiny structural changes can excite acoustic emission signals, therefore, incipient structure damages and its development from initial existence to failure can be obtained [68]. Acoustic emission is most frequently employed to fault diagnosis for WTBs, and statistical methods with different characteristics are also applied. Acoustic emission signals have high signal-tonoise ratio, and can be employed in high noise conditions [68]. Bouzid et al. [75] indicated that features extracted from acoustic emission signals were amplitude, acoustic energy, rise time, root mean square (RMS) value, kurtosis, counts, and so on. Additionally, features extracted from the envelopes of acoustic emission signals were mean value, peak value, arrival time and others, and this kind of features can be calculated while monitoring without the requirement of acquiring entire signals [75]. Signal analysis of acoustic emission can be applied to determine the status, the damage location and the failure mode of the blade. Yang et al. [29] concluded that variations of the blade mechanical properties, such as natural frequency, tip deflection and elastic modulus, were employed to indicate the structural integrity degradation. The results on blade failure diagnosis by using acoustic emission can be obtained by the comparison between the acoustic emission signature and the mechanical property of the turbine blade [29]. Tang et al. [73] showed that the crack growth was correlated with acoustic emission signals, and can be successfully detected. Moreover, the signal amplitude was regarded as the fault indicator with a detection threshold, and the detected fault signals can provide early warnings prior to the blade damage [73]. Purarjomandlangrudi et al. [85] employed variations of the time domain statistical parameters, such as skewness, kurtosis, and RMS, to demonstrate the existence of a fault, where RMS was proved to be sensitive to detect a fault [163]. Detection methods based on acoustic emission show good prospects on early warnings prior to the developing damage for a WTB, and are most commonly used. However, the detected acoustic emission signals may be composed of many sources other than the crack, and these noise signals may sometimes exceed the crack signals. Actions should be taken to eliminate the noise signals, such as setting a specific detection threshold [73]. 4.3. Fault indicators using ultrasound Ultrasonic signals can provide information of blade damages by characteristics including frequency, amplitude, standing waves, wavelength, phase velocity, and so on [26,91,92]. Park et al. [91] used a standing wave filter to identify and visualize a damage (delamination) from a GFRP blade, because standing waves were generated by delamination, debonding and others, and the variations of the waves could distinguish the damage type. Habibi et al. [92] employed the central frequency, wave length and phase velocity of ultrasonic waves to guide the ultrasonic wave in order to remove ice accumulation on the blade. Ye et al. [86] used the pulse-echo ultrasound to inspect internal damages of WTBs by employing parameters such as amplifier, sampling frequency, and so on, and a C-Scan image was also adopted to identify the defect. Park et al. [94] extracted reflection waves and calculated reflection energy values by using time-of-flight analysis for delamination localization of wind turbine blades. The value of reflection energy can be computed by [94]


Z REðPÞ ¼ EP þ PS

tþT=2

tT=2

W ðsÞ2 ds

ð3Þ

where REðPÞ represents the reflection energy of point P within t  T=2, and T is the size of the time window, T ¼ 1=f ; f is the lowest cut-off frequency; E is the excitation point, P is the damage point, and S is the sensing point, as shown in Fig. 11; t is the arrival time of the reflection wave, t ¼ vEPEP þ vPSPS , and EP is the distance between E and P, and PS is the distance between P and S, v EP and v PS are the wave velocity in the direction of EP and PS, respectively; W ðsÞ represents the ultrasonic response obtained by using the ultrasonic technique. Signal processing methods should be applied to extract more internal defects of the blade [26] such as time-frequency approaches like Wavelet transform, Wigner-Ville distribution, Hilbert-Haung transform, and the cross-correlation method [164] and others. However, due to the requirement of high spatial resolution, the inspection time of the large wind turbine blade will be extremely long [91]. Furthermore, the signal analysis and the fault diagnosis processes are also timeconsuming. Please cite this article as: Y. Du, S. Zhou, X. Jing et al., Damage detection techniques for wind turbine blades: A review, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2019.106445

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Fig. 11. Reflection energy calculation [94].

4.4. Fault indicators with vibration signals Vibration signals are often processed at three categories: frequency domain features, time domain features, and timefrequency domain features [165]. Fault indicators for vibration-based methods are features extracted from structural vibration responses [166,167], including frequency-response, amplitude, mode shape, strain energy, spectral kurtosis, and so on [119,121,166,168], and those integrated with non-probabilistic methods, time series methods [169,170], or artificial intelligence methods [113]. Spectral kurtosis is one of the important indicators for signal analysis of vibration-based technique especially for rotating machines, and the concept of kurtosis is expressed as (for details see Ref. [168])

K ðxÞ ¼

n o E ðx  lÞ4

r4

3

ð4Þ

where x is the time series; l; r are the mean and the standard deviation of x, respectively; Efg is the function for expectation. Strain energy is proved to behave better than other vibration response signals, which can avoid the interference of environmental factors, and can be expressed as (for details see Refs. [169,171])

1 U¼ 2

Z

L

EIz ðxÞ 0

!2 @2y @2x

dx

ð5Þ

where L is the length of the detected surface; x; y; z are the displacement on the x-axis, y-axis and z-axis, respectively; EIz is the flexural rigidity on the z-axis. It is known that the vibration features will change when the deterioration or damage occurs [172], but they are also affected by environmental or operational variations [129]. Therefore, Fault indicators are chosen in order to better distinguish damage signals from environmental and operating signals. Researches will be attempted to extract damagesensitive features by using signal processing approaches [173]. For instance, the application of Wavelet transform with both frequency-domain and time-domain is commonly utilized to capture fault information from the weak vibration signals [174–177]. Naderi et al. [178] developed a data-driven nonlinear approach by using only the frequency response data to detect and isolate the fault. Oliveira et al. [119] employed natural frequencies of the turbine blade based on vibration-based methods to perform damage detection, and regression models were utilized to reduce the influences of external factors on damage features. The change of frequency along the yaw angle may indicate the stiffness variation of the turbine blade in particular directions [119]. Ciang et al. [44] mentioned that the used of mode shape can obtain the structural behavior of the blade, and the residual of the mode shape can exhibit the dissimilarity between the healthy and damaged structure. Accordingly, the blade damage can be determined by comparing between the response from a normal state and that from a damage state. In addition, the deviation ratio, probability density, kurtosis, skewness, and so on, are also regarded as the evaluation parameters [179]. Wang et al. [102] adopted the time domain feature (the deviation ratio) to extract potential information from the vibration response data for fault diagnosis. The deviation ratio is employed to assess the relative difference between the two signals. Moreover, it is feasible to use nonlinearity detection for crack evaluation [180], due to a small damage will introduce nonlinearities into the structure [181]. Otherwise, a linear model can exactly describe the undamaged structure [166]. Nonlinear vibration-based techniques can detect the damage prior to crack initiation [182]. Nichols et al. [166] computed the mutual information and the transfer entropy applying nonlinearity indices from time series data of vibrational responses to detect the presence of damages in a structure. Vibration-based fault diagnosis is based on signal processing techniques, and can be employed to monitor complicated systems without prior knowledge, but the method will be slower than the model-based approach when detecting sudden Please cite this article as: Y. Du, S. Zhou, X. Jing et al., Damage detection techniques for wind turbine blades: A review, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2019.106445

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changes. Furthermore, vibration signals are always susceptible to noisy surroundings. Therefore, the reliability and accuracy of damage detection and fault diagnosis highly depend on the complex signal processing algorithms [101]. 4.5. Fault indicators based on thermography Thermography-based methods can detect structural damages of WTBs by monitoring variations in the thermodynamic properties of the blade, such as heat distribution [183], temperature distribution [133], thermal energy [184], and so on. Yang et al. [29] concluded that damages in the blade can be obtained by measuring temperature differences at the same detection point. Temperature signals are used to obtain anomaly detection for WTBs because the temperature will exceed certain values when the blade is subject to abnormal conditions. Therefore, the temperature variation caused by material or structural changes in different parts of the blade is applied in examining the blade condition. Hwang et al. [137,138] extracted the abnormal thermal responses to inspect the internal delamination of wind turbine blades, and the image reconstruction methods were applied to extract failure features. Image processing methods are required to obtain high-quality thermal images and detect subsurface damages, such as image enhancement, image reconstruction, and so on. Fault indicators of detection methods based on thermography can intuitively obtain the blade conditions through thermodynamic properties. However, the interpretation of thermograms will be disturbed and affected by environment factors, such as reflections, dirt, and so on [134]. Furthermore, the depth information of damage cannot be obtained by using thermography [137], thermal signals generated by small defects are not possible to be detected [139], and the inspection speed still needs to be improved. 4.6. Fault indicators in machine vision Images are the direct detection results by applying machine vision-based methods. As image processing technologies have been widely employed to identify images of different objects, this technique shows the attempts and application potential for condition monitoring and structural damage inspection. Fault indicators are the features such as area, length, shape, texture, Haar-like features, and others, which can be obtained from the images. A Haar-like feature is employed to describe the difference between white pixels and gray rectangle pixels and crack detection can be performed by stage classifiers [152]. Image processing methods, such as binarization, threshold segmentation and identification, are adopted to realize defects or damages of the turbine blade. The deformation, distributed strain, deflection, displacement and modal parameters of the

Table 8 Summary of the fault indicators and the related feature extraction methods. Technique

Fault indicators

Feature extraction methods

Strain measurement [58,60,62,63]

Strain Peak strain Strain rate Deflection Bragg wavelength Acoustic emission signals (mean value, peak value, etc.) Waveform characteristics (rise time, amplitude, etc.) Acoustic energy RMS Frequency Amplitude Time-of-flight Reflection energy

Chi-square distribution Experimental modal analysis DPCA/NLDPCA feature information fusion

Acoustic emission [67,68,73,75,85]

Ultrasound [86,91,26,94,164]

Vibration [113,119,121,166,168]

frequency-response amplitude mode shape strain energy spectral kurtosis

Thermography [137–139]

Temperature Thermal energy Thermal responses

Machine vision [143,152,159]

Haar-like features Damage edge feature

Statistical analysis Envelope analysis FFT analysis

Time-of-flight analysis Wavelet transform Hilbert-Haung transform Wigner-Ville distribution cross-correlation methods Frequency domain methods Wavelet transform Spectral kurtosis filtering Time series analysis regression models empirical model decomposition Artificial intelligence methods Fast Fourier Transform Statistical pattern recognition Image enhancement Image reconstruction Digital image correlation Image restoration Image reconstruction Image enhancement Image segmentation

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blade can be extracted from the captured images [150]. The steps involved in image processing techniques are: (1) Images of the blade either in 2-dimensional or in 3-dimensional are acquired; (2) Binary (gray scale) images obtained from the original images are obtained; (3) Edge segmentation methods (threshold, edge detectors, and so on) and binary morphology are used to separate the defect or damage from the background; (4) Structural displacement, strains or other fault indicators can be obtained; (5) These features are thus employed to evaluate the blade health [143,159]. Fault indicators of the detection methods based on machine vision are features extracted from captured images, and are available for online monitoring. The images are easily obtainable for full-field measurement. However, the accuracy of feature extraction is dependent on image processing techniques, including image restoration, image reconstruction, image segmentation, image recognition and so on. Furthermore, the relationship between the feature and the damage is yet to be resolved. 4.7. Summary of the fault indicators and the related feature extraction methods Fault indicators can be acquired through direct measurements by using the aforementioned detection techniques, or by means of feature extraction methods on account that it is non-convenient to obtain damage-sensitive features. Summary of the fault indicators and the related feature extraction methods is listed in Table 8. 5. Discussions In previous sections, progresses in damage detection techniques for WTBs have been summarized. The comparison of detection techniques for WTBs with respect to monitoring types, on-line/off-line, cost and precision, is listed in Table 9. The detection techniques based on machine vision, strain measurement, and acoustic emission, are available for continuous on-line monitoring [30,12]. The ultrasonic technology can implement remote inspection when regarding laser as both the transmitter and receiver with high costs, and be available for on-line monitoring [44]. The traditional ultrasonic technology requires surface contact [66], and so does the traditional vibration-based technique. The detection techniques based on thermography and machine vision are free of surface contact [150], but the methods based on strain measurement and acoustic emission are strongly dependent on surface contact [25]. Moreover, the thermography-based and vibration-based techniques are limited for continuous monitoring [25,27,29]. Although the techniques are available for on-line monitoring, it is still in laboratory researches. Strain measurement and acoustic emission are the only techniques that have already applied in real WTB monitoring. In spite of extensively developed and utilized, most inspection methods are suitable for post-production inspection before blade installation [12], and there is still no single best damage detection technique available for the WTB inspection with satisfied results in operation. Therefore, scholars have integrated two or more detection techniques into fault diagnosis, such as acoustic emission and vibration [185], piezoelectric sensor and machine vision [155], infrared thermography and machine vision [156], and so on. Facing encountered problems and reliability requirements, the trend of damage detection techniques has been moved toward full-field, non-contact [28], wireless and on-line inspection, or even other directions [31]. As can be found from the review, it is very difficult to detect full-scale blade damages by using contact techniques due to wiring and the limit of the small detection area, such as the techniques based on strain measurement, acoustic emission and traditional ultrasonic methods. This difficulty arises on account of the layout complexity, the cost in terms of finance and installation time, or failures of sensors under cyclic loads. Additionally, damage detection techniques based on acoustic emission, ultrasound, thermography and machine vision are non-destructive testing (NDT) methods. WTBs are subjected to varieties of environmental and operational variations at the same time, significant challenges will be imposed to the damage detection techniques. As for wind farms that are not easy to reach, such as offshore wind turbines, contact techniques are not applicable, and it will be better to apply techniques based on thermography, machine vision and other remote approaches to achieve the turbine blade detection. As for wind turbines that are working in high temperatures and variational humidity, the accuracy of the thermography-based technique will be reduced. The variations of temperature and other environmental conditions could change the stiffness of blade structure, alter the blade boundary conditions, or influence the dynamic properties of WTBs resulting in noisy signals, which may cause ambiguities in the results of blade changes caused by damages [129,131]. It is necessary to test the WTB in its real operating conditions and environments to collect a wider range of datasets in order to better implement damage detection [129]. For

Table 9 Comparison of different damage detection techniques for WTBs [8,25,27,44,46,66,99,134]. Technique

Types of monitoring

On-line available

Cost

Precision

Strain measurement Acoustic emission Ultrasound Vibration Thermography Machine vision

Embedded Contact Contact/Non-contact Contact/Non-contact Non-contact Non-contact

Yes Yes Yes Yes No Yes

Low/High High High Medium High Low

 1 cm <1 cm  1 cm <1 mm 3–5 mm  1 mm

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wind turbine blades in operation, the rotation could be a big challenge for on-line monitoring and damage detection. Therefore, with full-scale, non-destructive, wireless and on-line monitoring requirements, the technique based on machine vision demonstrates the potential and advantages on the inspection and health management of WTBs due to its advantages of noncontact, high precision and long distance detection [143], as well as the capability to measure dynamics of rotational structures [186], but has not been widely implemented in industry yet. Moreover, capturing high-definition images and extracting damage information under the condition of rotation and complex environments require a lot of efforts in the future work. 6. Research prospects of WTB damage detection techniques To meet with industrial requirements in WTB inspection and maintenance, we believe that the future research trend may involve the following aspects: (1) With the development of machine vision and image processing approaches, the detection technology based on machine vision becomes a promising and core technique for the surface damage detection and deformation measurement [28] of WTBs because of the low-cost, easy operation and free of the need for prior knowledge on damage positions. Researchers have widely used this technology to achieve surface damage detection of WTBs or other large components. Since the detection accuracy can be readily improved by applying specialized image collection devices and image processing approaches [142], future works should focus on image processing and related algorithms, simultaneous localization and mapping, machine learning for the damage recognition and so on. Besides, the UAV-based detection system using simultaneous localization and mapping methods is regarded as a promising approach for the dynamic measurement and on-line blade inspection. (2) It is better to use at least two of the detection techniques based on acoustic emission, ultrasound, machine vision and others, to comprehensively detect the blade and find different types of damages in different areas. However, the principal purpose of the approach is not the damage detection itself, but the fault prediction and fault diagnosis of WTBs for the health management and maintenance, as well as to obtain a better understanding of failure mechanisms. Additionally, the investigation on the damage mechanism is of primary importance, where the inter-relationship between the damage and the remaining useful life of the turbine blades still remains unclear. Therefore, damage detection techniques should be applied to elucidate the damage mechanism and the lifespan reduction of turbine blades resulted from damages. (3) Damages in a structure may bring nonlinear characteristics to the material [187], and nonlinear problems, such as stress concentration, local buckling, and others, may cause failures to the blade [58,188]. When the blade is damaged, the response will contain nonlinear characteristics that can identify the presence of the damage. Therefore, damage detection methods based on nonlinear principles in combination with the aforementioned technologies can be employed to detect the damage. Compared with mathematical modeling, model-based methods and other approaches, damage detection methods based on nonlinear principles have high accuracy and robustness by fully using the nonlinear features. Nonlinear fault feature extraction methods were employed in pattern recognition to implement SHM for WTBs, such as nonlinear autoregressive with exogenous (NARX), Hierarchical Non-linear Principal Component Analysis (NLPCA), and others [33,189]. Harmonic response, sub-harmonic response, nonlinear resonance, and so on, are nonlinear features associated with the occurrence of a damage [181]. Nowadays, more and more approaches, such as nonlinear ultrasonic techniques [190], nonlinear acoustic techniques [191], nonlinear vibration-based techniques [182], image processing techniques [192], and so on, employ nonlinear characteristics as fault indicators. Nonlinear ultrasonic techniques [33,190] are sensitive to micro-damage than linear techniques, damage localization [193] and micro fatigue cracks detection [194] can be implemented by using nonlinear features of Lamb waves. Since Damaged surfaces may exhibit high acoustic nonlinear characteristics [191], nonlinear acoustic techniques can be employed to obviously detect blade damages, but the authors also mentioned that future researches should be addressed to distinguish the nonlinear characteristic caused by cracks from that due to the unknown sources. Nonlinear vibration-based techniques can detect and estimate fatigue damages precursor by measuring variations of the nonlinear responses in the structure [182]. Detection technique based on vibro-acoustic modulation is proved to effectively detect cracks in composite materials by measuring the increase in nonlinear characteristics, and is rarely affected by varying environmental and operating conditions [187], which makes up the disadvantage of the traditional vibration-based technique. In addition, the relationship between fault indicators and failure mechanism should be resolved in the future. (4) Damage detection techniques require cable installations and power sources to achieve their works. Since WTBs operate in rotation, wireless transmission of signals and wireless detection have been required for on-line damage detection [9]. Although some detection sensors/devices mounted on the blade can obtain power from the wind turbine, techniques that detect damages from remote areas require a means of external power sources, which is either inconvenient or costly. Moreover, it is impossible to detect blade damages for detection techniques that are powered by the wind turbine when the turbine is not in operation. Developing self-powered intelligent damage detection devices may be interesting and wireless for blade damage detection. In the past ten years, energy harvesting technique that converts ambient environmental energy into electric energy is receiving more and more attention [195–198]. This technique has a great application potential to power wireless sensor networks and small electromechanical devices [199]. For example, existing microscale piezoelectric vibration energy harvesters can output power in Milliwatts level, and they can be installed in the

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remote area to harvest ambient vibration energy to provide a permanent power supply [200–202]. Wind-based energy harvesters were developed to efficiently harvest energy from low speed wind [203,204]. It may be suitable to power the intelligent damage detection devices/sensors around the wind turbine or carried by the devices/sensors, and this will be another research interest for the field of WTB damage detection. For the small-scale energy harvesting, portable electromagnetic energy harvesters with the output power in Watt level were also designed for powering wireless devices [205,206]. In addition, solar energy generators can be complementary with vibrational energy harvesters to provide a reliable power source for self-powered intelligent damage detection devices [207]. Therefore, in the future work, design and integration of energy harvesters with damage detection devices for wind turbine blades are interesting and challenging issues, and are potential future directions. 7. Conclusions Damage detection of the blade is considerably important to blade inspection during operation, because the size and the structural complexity of turbine blades have increased significantly over the past decade and the maintenance has resulted in high expenses. This paper has presented a review of damage detection techniques based on strain measurement, acoustic emission, ultrasound, vibration, thermography and machine vision for blade inspection. In particular, detection principles, development methods, pros and cons of each technique with comprehensive and up-to-date information, and their fault indicators are reviewed. In addition, promising techniques on the damage detection of WTBs are pointed out and possible future trends are discussed via a comparative analysis. The tendency toward damage detection for WTBs is the requirements of full-scale, remote, non-contact, non-destructive, wireless, and on-line monitoring. It is increasingly important to early detect the blade damage, and continuously assess the structural health of WTBs. Machine vision-based approach is considered as a potential and effective method for continuous surface inspection of WTBs, and researches on image processing and related algorithms are strongly recommended and required. Additionally, this paper proposes a new idea for the design and integration of energy harvesters and damage detection methods of WTBs, where the devices are self-powered and wireless. Although this state-of-art review on damage detection techniques of WTBs is not able to cover all related topics in great detail, we hope it can provide valuable information to practitioners in the field. 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. Acknowledgements This project has been supported by the National Natural Science Foundation of China (Grant No. 11802237), the Fundamental Research Funds for the Central Universities (Grant No. G2018KY0306), Natural Science Foundation of Guangdong Province, China (Grant No. 2018A030310522), and the Shenzhen Science and Technology Project, China (Grant No. JCYJ20170818100522101). References [1] A. Tummala, R.K. Velamati, D.K. Sinha, V. Indraja, V.H. Krishna, A review on small scale wind turbines, Renewable Sustain. Energy Rev. 56 (2016) 1351–1371. [2] H.D.M. de Azevedo, A.M. Araújo, N. Bouchonneau, A review of wind turbine bearing condition monitoring: state of the art and challenges, Renewable Sustain. Energy Rev. 56 (2016) 368–379. [3] X. Chen, R. Yan, Y. Liu, Wind turbine condition monitoring and fault diagnosis in china, IEEE Instrum. Meas. Mag. 19 (2) (2016) 22–28. [4] M. Fatehi, M. Nili-Ahmadabadi, O. Nematollahi, A. Minaiean, K.C. 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