remote inspection of internal delamination in wind turbine ... · remote inspection of internal...

Vol.:(0123456789)

International Journal of Precision Engineering and Manufacturing-Green Technology (2020) 7:699–712 https://doi.org/10.1007/s40684-020-00192-9

1 3

REGULAR PAPER

Remote Inspection of Internal Delamination in Wind Turbine Blades using Continuous Line Laser Scanning Thermography

Soonkyu Hwang1 · Yun‑Kyu An2 · Jinyeol Yang3 · Hoon Sohn1

Received: 25 March 2019 / Revised: 30 September 2019 / Accepted: 9 January 2020 / Published online: 22 January 2020 © Korean Society for Precision Engineering 2020

AbstractThis study proposes a continuous line laser scanning thermography (CLLST) system for remote inspection of internal delamination in wind turbine blades. The CLLST system offers the following advantages: (1) remote delamination inspection can be achieved by mechanically scanning a line laser beam and simultaneously capturing the corresponding thermal waves in nondestructive and noncontact manners; (2) internal delamination and surface damages can be classified by analyzing laser-induced thermal wave propagating patterns; (3) instantaneous delamination detection and quantification can be accom-plished without using baseline data which is previously collected from the pristine condition of a target blade. To examine the feasibility of the CLLST system, laboratory and full-scale tests were performed using a carbon fiber reinforced polymer (CFRP) plate, a 10 kW glass fiber reinforced polymer (GFRP) wind turbine blade, and a 3 MW GFRP wind turbine blade. The test results demonstrated that the 10 mm diameter internal delamination located 1 mm underneath the blade surface was successfully detected even 10 m far from the target blade with a laser scanning speed of 2 mm/s.

Keywords Delamination detection · Wind turbine blade · Remote inspection · Nondestructive testing · Line laser scanning thermography

1 Introduction

Composite materials have been mainly used to manufacture wind turbine blades due to their unique structural character-istics, such as light weight, high strength, durability, and cor-rosion resistance [1, 2]. However, the composite materials are often vulnerable to delamination between the inner layers because they are composed of multiple laminate layers with epoxy resins [3]. The delamination caused by low-velocity impact or fatigue loading is often barely visible from the target surface [4], making it difficult to be detected. Once delamination is formed, it can rapidly propagate through the interface between inner layers when subjected to repeated loadings [5], which reduces the stiffness of the part [6]. For

example, if the void volume inside a composite structure increases from 1 to 3%, its stiffness decreases up to 20%. Such stiffness reduction of a single delaminated member can cause stress concentration at this weak point, which eventu-ally leads to structural failure. Therefore, early detection of delamination is crucial to guarantee the safety and integrity of wind turbine blades.

To detect delamination, several nondestructive testing (NDT) techniques have been proposed [7–11]. One of the most extensively used NDT techniques is ultrasonic wave imaging. Once ultrasonic waves are generated using an ultra-sonic transducer or Q-switched pulse laser, the ultrasonic waves propagate through the target structure. Simultane-ously, time-varying ultrasonic wave images can be obtained by spatially scanning an air-coupled transducer (ACT) [12] or sensing lasers [13–16]. Alternatively, the similar ultra-sonic wave imaging can be accomplished by spatially scan-ning the Q-switched pulse laser and measuring the ultrasonic responses at a single spatial point using ACTs or sensing lasers [17]. When the propagating ultrasonic waves encoun-ter delamination, the ultrasonic waves are scattered from the delamination boundaries. Then, the delamination can be identified by analyzing the scattered wave fields. However,

Online ISSN 2198-0810Print ISSN 2288-6206

* Yun-Kyu An [email protected]

1 Department of Civil and Environmental Engineering, KAIST, Daejeon, South Korea

2 Department of Architectural Engineering, Sejong University, Seoul, South Korea

3 Quality Team, Test and Package Center, Samsung Electronics, Cheonan, South Korea

https://orcid.org/0000-0003-2916-4917

http://orcid.org/0000-0003-3852-9025

https://orcid.org/0000-0001-8090-1981

https://orcid.org/0000-0001-9337-6653

http://crossmark.crossref.org/dialog/?doi=10.1007/s40684-020-00192-9&domain=pdf

700 International Journal of Precision Engineering and Manufacturing-Green Technology (2020) 7:699–712

1 3

its applications have been often limited due to prohibitively long scanning time, low signal-to-noise ratio, and complex signal processing. Moreover, since the ultrasonic waves are highly attenuated in composite materials, its inspection area is often limited. Recently, a terahertz (THz) spectroscopy has been emerged for imaging delamination on wind turbine blade [18, 19]. Since THz waves are able to penetrate com-posite materials approximately 100 mm from the surface and have high time resolution, internal delamination detectability can be improved [18, 20]. However, the quite limited work-ing distance of the THz spectroscopy inspection from the target surface and its bulky device hinders field applications. X-ray radiography has also excellent spatial resolution and high penetration capability to visualize internal delamination [21]. However, its field application is often limited due to health and safety concerns [22] and huge power consump-tion for its operation. Furthermore, the X-ray radiography requires accessibility to both sides of the structure, and such access may not be feasible for wind turbine blades.

To overcome the aforementioned technical limitations, infrared (IR) thermography techniques have been proposed for fully noncontact inspection of delamination in wind turbine blades. The IR thermography techniques visualize delamination by measuring the interactions of thermal waves with the delamination. These IR techniques are simple, fast, noninvasive, and provide intuitive visualization of delamina-tion [23, 24]. Various heat sources such as halogen lamps, xenon lamps, flash lamps, ultrasonic, microwaves, and eddy currents are utilized to generate thermal waves that are inci-dent upon a large target area [25–30]. However, the position and intensity of irradiation from these heat sources cannot be controlled precisely, and the working distance between the heat source and target structure must typically be in the order of tens of centimeters [28]. Although Moran et al. recently tried to extend the inspection distance of the IR thermog-raphy by using a halogen lamp with a specially designed elliptical reflection panel, the working distance was still limited to 500 mm due to the difficulty in beam divergence control [31]. Laser has emerged as an attractive heat source for the IR thermography techniques because the position and intensity of the laser beam can be precisely controlled, and the laser beam can be transmitted over a long range [32]. However, because a point laser beam produces heat only within a very localized area (less than 1 cm2 ), scanning a large target area requires a long time. An et al. and Li et al. recently modified the shape of the laser beam from a point to a line and grid for accelerated inspection [33–35].

In this study, a continuous line laser scanning thermogra-phy (CLLST) system is proposed for remote and automated visualize of internal delamination in wind turbine blades. The CLLST system has technical advantages as follows: (1) large-scale wind turbine blades can be rapidly and remotely inspected by mechanically scanning a line laser beam in a

noncontact manner; (2) the target inspection area and heat intensity can be precisely controlled to avoid undesired sur-face ablation; (3) Only internal delamination can be auto-matically extracted and visualized without reference data by analyzing laser-induced thermal wave propagation patterns regardless of other surface disturbances. The performance of the proposed CLLST system is validated through laboratory-scale and full-scale tests on glass-fiber-reinforced polymer (GFRP) wind turbine blades as well as a carbon-fiber-rein-forced polymer (CFRP) plate.

The remainder of the paper is organized as follows. Sec-tion 2 describes the hardware configuration and working principles of the CLLST system. Then, an instantaneous delamination visualization algorithm is proposed in Sect. 3. In Sect. 4, the performance of the proposed CLLST system is validated experimentally using a CFRP plate and a 10 kW GFRP wind turbine blade. Furthermore, its applicability to the 3 MW GFRP wind turbine blade is investigated in Sect. 5. Finally, the paper is concluded with summary and discussion in Sect. 6.

2 Development of the CLLST System

Figure 1 illustrates the configuration of the CLLST system, which consists of three modules: (1) the excitation module that is composed of a continuous wave (CW) laser, a cylin-drical lens, a beam collimator, and a galvanomotorized scan-ner; (2) the sensing module comprising an IR camera and a focusing lens; and (3) the control module with device control and data processing algorithms. Initially, the control module sends out control and trigger signals to the CW laser in the excitation module. Further, the CW laser emits a point laser beam having a Gaussian profile, and then the cylindrical lens transforms the point laser beam into a line laser beam. Simultaneously, the control module sends out a control sig-nal to the galvanomotorized scanner, and the galvanomo-torized scanner controls the line laser beam position with a constant speed to scan the target structure surface continu-ously along the desired direction. Then, the thermal waves are generated on the target structure surface, and the corre-sponding thermal responses are captured from the IR cam-era in the sensing module. Here, the excitation and sensing modules are synchronized by the trigger signals generated from the control module. The field of view (FOV) of the IR camera can be adjusted by controlling the distance between the IR camera and the target structure with adjusting focal distance using the focusing lens mounted in front of the IR camera. The captured IR images are transmitted to the con-trol module and processed using the algorithm described in the succeeding section. Note that the image acquisition and processing are performed automatically using LabVIEW® and MATLAB® software installed on the control module.

701International Journal of Precision Engineering and Manufacturing-Green Technology (2020) 7:699–712

1 3

Figure 2 illustrates how thermal wave propagates along the surface and thickness directions, as the laser beam scans the structure surface. Using a mechanical scanning device, i.e., a galvanomotorized scanner, the target surface is scanned by the line laser beam. When the target surface is exposed to the scanning line laser beam, thermal waves are generated due to the photothermal effect, and these waves propagate simultaneously along the surface and through the thickness as depicted in Fig. 2. Note that thermal wave propagation along the composite mediums is typically lim-ited to a short distance due to the low thermal conductivity ( k ) of composite materials. For example, typical CFRP and GFRP structures composed of the multiple layers bonded with epoxy resin (0.2 Wm−1K−1 of k ) have k values less than those of steel by at least one order of magnitude (approxi-mately 43.0 Wm−1K−1 ). Thus, in this study, thermal waves on the structure surface are created by scanning the line laser beam along the target surface. Here, the line laser beam intensity should be carefully determined by considering the properties of the composite materials and scanning speed [36]. The scanning laser beam inherently creates heating and cooling cycles at each point on the surface, causing thermal waves to propagate through the thickness. Therefore, the proposed CLLST system can detect both surface damage and internal delamination in composite structures.

The k value of the delaminated area is much smaller than that of the intact area since delamination causes the formation of air pocket (approximately 0.024 Wm−1K−1 for k ) between the laminates. When a surface damage occurs on the struc-ture, likewise, material discontinuity occurs on the surface and the portion is filled with the air void. The sudden change of k at the surface damage and delamination boundary disturbs thermal wave propagation. Thus, the surface temperatures on the surface damage and delamination area indicates higher than the intact area right after the line laser beam scanning as depicted on Fig. 3. However, the surface temperature varia-tions after the line laser beam scanning also change depending on the depth of the damage. In case of the surface damage, thermal wave disturbing phenomena on its interface can be directly measured from the structural surface, showing higher surface temperature than the delamination area. After then, the thermal waves on the surface damage area dissipates much faster than the delamination area, because the direct contact with the external air and the surface damage area accelerates thermal wave dissipation. Thus, it is possible to extract the

Fig. 1 Schematic of the continuous line laser scanning thermography (CLLST) system

Fig. 2 Propagation of laser-induced thermal waves along the surface and through the thickness using a scanning line laser beam

Fig. 3 Thermal wave propagating patterns after line laser beam scan-ning according to the structure conditions, i.e. intact, internal delami-nation and surface damage


1 3

delamination area by analyzing the thermal wave propagating patterns in the time domain as shown in Fig. 3.

3 Development Of The Instantaneous Delamination Visualization Algorithm

Figure 4 depicts the overview of the proposed instantaneous delamination visualization algorithm. The main objective of the proposed algorithm is to visualize the internal delamina-tion from the IR images measured by the CLLST system. Here, the IR images are presented in Cartesian coordinates consisted of the horizontal axis ( x ) and the vertical axis ( y ). The surface temperature variation after exposure to the line laser beam can be expressed as a function of time ( t ) [37]:

where ΔT is the temperature variation, i.e. decaying pattern, after the heating of the target surface, Q is the heat energy per unit length in the line laser beam, � is the density of the target material, C is the specific heat of the material, t is the time, and r is the distance from the center of the

(1)ΔT(t) =Q

4�C�ktexp

(

−r2

4kt

)

line laser beam. As described in Eq. (1), the temperature is decayed after a round of heating, and the ΔT decaying pattern is altered by delamination caused by the change of k as explained in Chapter 2. The delamination-induced ΔT decaying pattern can be extracted from the IR images using the mechanism described below.

3.1 Step 1: Construction of ROI Image from IR Image.

When the line laser beam continuously scans the target structure along the x direction, the thermal responses within FOV are captured as raw IR images in the time domain. First, to calculate the temperature variation ( ΔT ) induced by the line laser beam scanning, the initial tem-perature ( T0 ) is subtracted from the surface temperature ( T ) through the measured raw IR images. By calculating ΔT from each spatial point of the raw IR images, the IR images are constructed as depicted in Fig. 5a–c. Since the scanning speed ( v ) is controlled at a constant speed from the CLLST system, the x coordinate of the line laser beam is modified to vt , as depicted in Fig. 5c. Since the line laser beam scans the target surface, the thermal response at a

Fig. 4 Overview of the instan-taneous delamination visualiza-tion algorithm: Step 1-con-struction of region of interest (ROI) image from IR image, Step 2-extraction of abnormal-ity image, and Step 3-noise removal


1 3

specific point in the target surface looks similar to the ther-mal response induced by a pulse laser beam exciting the point, as depicted in Fig. 5d. The temperature exhibits a sudden increase to its peak value ( ΔTp ) at time tp , and then decays gradually back to the initial value. The temperature delay follows the decaying pattern as according to Eq. (1), and the parameter Q , � , C , and k in Eq. (1) is obtained by fitting a decaying pattern to ΔT calculated from T meas-ured during the cooling cycle. Here r is defined as 0 which means the center of the line laser beam in the y-axis. The end of the cooling cycle ( td ) is defined as the moment when the cumulative density function of the fitted decay-ing pattern reaches 0.95, as shown in Fig. 5e. The ending temperature ( ΔTd ) and width ( w ) of the region of interest (ROI) corresponding to td are decided, as illustrated in Fig. 5f. Finally, the ROI image at a specific t is defined in terms of w and h , as depicted in Fig. 5g. Here, the height ( h ) is decided based on the vertical length of the line laser beam. Based on the thermal wave propagating patterns explained in Chapter 2, ΔT on the delamination and sur-face damage region becomes much bigger than the intact region right after the laser scanning ( t = tp ). However, only the temperature on the delamination region becomes dif-ferent from that of intact region as time passes after the laser scanning ( t > tp).

3.2 Step 2: Extraction of Abnormality Image

In this section, the ROI images are processed to extract an abnormality image, which highlights the ΔT decaying pat-terns that were caused by delamination. Initially, the tem-perature decay, Eq. (1), is estimated. Since the laser beam is scanned over a fixed target surface at v,t can be represented as x∕v . Further, the temperature decay image representing time decay ( ΔT ) can be estimated by averaging the ther-mal responses that were obtained from all ROI images, as depicted in Fig. 6a.

Furthermore, the detrended images that accentuate the abnormal temperature ( Ta ) are obtained by subtracting the temperature decay image from each ROI image, which is depicted in Fig. 6b. Note that all Ta in a detrended image will be close to zero if there is no delamination. Each detrended image is subsequently assigned to the original position of the corresponding IR image, as illustrated in Fig. 6c. Fur-ther, the unassigned portion of the reconstructed ROI image is zero-padded. Finally, an abnormality image is obtained by adding all the reconstructed ROI images along the time

(2)ΔT =Q

4�C�k×1

t=

Q

4�C�k×v

x

Fig. 5 Determination of the ROI image: a Raw IR image with line laser beam excitation at a specific time ( t ), b raw IR image before line laser beam excitation, c the resultant IR image subtracted by b from a at a specific t . h and vt denote the height of the ROI image and the x coordinate of the line laser beam, respectively, d temperature variation ( ΔT ) in the time domain at a specific position of the target

surface. tp is the time when the surface temperature reaches the peak ΔT ( ΔTp ), e determination of the thermal dissipation time ( td ) from the cumulative density function (CDF) of temperature during the cooling cycle, f computation of the dissipated temperature ( ΔTd ), cor-responding to td , and g determination of the ROI image area defined by width ( w ) and height ( h)


1 3

domain and normalized with the maximum value, as illus-trated in Fig. 6d.

3.3 Step 3: Noise Removal

The abnormality image often includes undesirable noise components that are produced by surface patterns, ambient temperature gradients, and measurement noise along with the signals from delamination, as depicted in Fig. 7a. Binary imaging and median filtering are performed on the abnor-mality image to eliminate the undesired noise components. Initially, the binary image is extracted from the abnormality image using a threshold value, as depicted in Fig. 7c. The threshold value is obtained by fitting a Weibull distribution to all the pixel values of the abnormality image and finding

the pixel value corresponding to a one-sided 99% confidence interval in the upper tail, as illustrated in Fig. 7b. Even after binary imaging, some sporadic noise remains in the binary image, and they are removed by applying a median filter with a 3 × 3 kernal as depicted in Fig. 7d. The final image visualizes delamination without any noise components as illustrated in Fig. 7e.

4 Laboratory‑Scale Tests

This section describes the laboratory-scale tests to validate the performance of the proposed CLLST system and the instantaneous delamination visualization algorithm. In order to show the applicability of CLLST system on the wind

Fig. 6 Extraction of the abnor-mality image: a construction of the temperature decay image by averaging the ROI images, b extraction of the detrended images by subtracting the temperature decay image from each ROI image, c assembly of the reconstructed ROI image by reassigning the detrended image back to the original portion of the corresponding ROI image and by assigning zeros to the remaining portion of the ROI image, and d construction of the abnormality image by adding all the reconstructed ROI images along the time domain

Fig. 7 Visualization of delamination after denoising: a the abnormality image, b the threshold calculation process, c the binary image, d the median filtering process, and e the final image


1 3

turbine blades composed of various composite materials, the 10 kW GFRP wind turbine blade as well as the CFRP plate were used as target structures for the validation tests. In addition, the 1 m and 10 m distance inspection test results on the 10 kW GFRP wind turbine blade are compared to show the remote inspection capability of the proposed CLLST system.

4.1 Description of Test Structures

The CFRP plate depicted in Fig. 8a has dimensions of 500 × 500 × 2mm3 and consists of 12 plies with a layup of [−45◦,+45◦] 6 T and a flat surface. Note that, the CFRP plate was cured via autoclave processing from 12 sheets of WSN3K [38] carbon fiber prepregs. Two different regions of 250 × 100mm2 on the CFRP plate were inspected, as illustrated in Fig. 8a. Inspection area II includes an internal delamination that is simulated by inserting a 25 mm diameter

and 150 μm thick piece of Teflon tape between the 6th and 7th layers. The CFRP material has k of 6.4 Wm−1K−1 , whereas the Teflon tape has k of 0.25 Wm−1K−1 . The 10 kW GFRP wind turbine blade depicted in Fig. 8b has dimensions of 3500 × 500 × 3mm3 and consists of six curved plies with a layup of [−45◦, 0◦,+45◦] S. Here, the 10 kW GFRP wind turbine blade was cured via autoclave processing from six sheets of glass fiber prepregs. Note that, the details of the material compounds were unknown to the authors due to the confidential issues of the blade manufacturer. Similarly, two different areas, one intact and the other containing an internal delamination, were inspected. A 10 mm diameter Teflon tape was inserted between the 3rd and 4th layers to simulate the internal delamination in inspection area II. The GFRP material has k of 3.0 Wm−1K−1 , whereas the Teflon tape has k of 0.25 Wm−1K−1 . Additionally, surface damage was introduced on the surface to simulate an external impact.

Fig. 8 Target structures used for laboratory-scale tests: a the CFRP plate and the corresponding inspection areas and b the 10 kW GFRP wind turbine blade and the corresponding inspection areas


1 3

4.2 Test Setup

Figure 9 illustrates the laboratory-scale test setup for the pro-posed CLLST system. Initially, the control computer sent out trigger and control signals to the CW laser (TMA-532-15 T, TMA) and the laser driver, and the CW laser continuously emitted the point laser beam having a diameter of 4 mm and a wavelength of 532 nm. The point laser beam was trans-formed into the line laser beam using the cylindrical lens and the beam collimator. Once the line laser beam reached on the target surface, the beam size was observed to be 100 mm long and 1 mm wide. The galvanomotorized scanner and IR camera (A6700SC, FLIR) mounting the focusing lens with angle of view of 15◦ were set to be 1000 mm apart from the target structure. The line laser beam scanned the designated inspection region driven by the galvanomotorized scanner and generated thermal waves at a constant scanning speed of 20 mm∕s . The intensity of the line laser beam was set to 92.31 mW∕mm2 and 115.38 mW∕mm2 for the CFRP plate and the 10 kW GFRP wind turbine blade, respectively. Here,

the inspection speed was calculated as 20 cm2∕s by dividing ROI (250 × 100 mm2) by the scanning time (12.5 s). Note that the inspection speed is defined as ROI of the IR camera per unit scanning time. The corresponding thermal responses were measured in the time domain using the IR camera. The IR camera has temperature resolution of 0.02 K, spatial reso-lution of 390 µm, sampling rate of 50 Hz, and measurable spectral range from 3 to 5 µm (mid-wave infrared).

4.3 Test Results

Inspection areas I and II located on the CFRP plate, which are displayed in Fig. 8a, were examined for 12.5 s. Once the IR images were measured, the ROI images and reconstructed ROI images were subsequently computed, as depicted in Fig. 10. The ROI images were extracted from the IR images of inspection area I with w of 272 pixels and h of 256 pix-els, as illustrated in Fig. 10a. The ROI images that were obtained from inspection area II had w of 300 pixels and h

Fig. 9 Laboratory-scale test setup of the proposed CLLST system

Fig. 10 IR images obtained from a inspection area I and b inspection area II, and recon-structed ROI images obtained from c inspection area I and d inspection area II of the CFRP plate at 12 s


1 3

of 256 pixels, as depicted in Fig. 10b. The IR image depicted in Fig. 10b illustrates that the delaminated region attained a higher temperature than the surrounding intact area due to its small k value. This temperature variation caused by the delamination ( Ta ) was further accentuated in the recon-structed ROI image at 12 s as depicted in Fig. 10d, once the temperature decay that was produced by the line laser beam was eliminated from the ROI image at 12 s. Conversely, no significant temperature variation was observed in the recon-structed ROI image that was acquired from the intact inspec-tion area I depicted in Fig. 10c.

Figure 11a, b show the abnormality images that are con-structed by summing up all the reconstructed ROI images that were obtained from inspection areas I and II of the CFRP plate, respectively. The corresponding final images that are obtained by applying the binary imaging and median filters to the abnormality images are displayed in Fig. 11c, d, respectively. In the intact inspection area I depicted in Fig. 11c, the surface patterns depicted in Fig. 11a are suc-cessfully suppressed, whereas the delamination in inspection

area II is clearly visualized in Fig. 11d. The number of pixels inside the delamination area was observed to be 3027 pixels, and the delamination size was estimated to be 460.407 mm2 , which depicts an error rate of 6.15% with respect to the actual delamination size.

Similarly, inspection areas I and II located on the 10 kW GFRP wind turbine blade displayed in Fig. 8a were also examined for 12.5 s. The test setup and image processing sequences for the 10 kW GFRP wind turbine blade inspec-tion were same as the setup in the CFRP plate test, except for the intensity of the line laser beam. The surface pat-terns shown in the abnormality images of Fig. 12a, b were successfully eliminated in Fig. 12c, d. Only the delamina-tion area is clearly highlighted in the final image presented in Fig. 12d. Further, no positive false alarm is indicated in the intact and surface damage areas, i.e. inspection areas I and II, as shown in Fig. 12c, d, respectively. The number of pixels representing the delamination area in the inspection area II is 2369 pixels, and the corresponding physical size is estimated to 360.325 mm2 which depicts an error rate of

Fig. 11 Abnormality images obtained from a inspection area I and b inspection area II, and final images obtained from c inspection area I and d inspec-tion area II of the CFRP plate

Fig. 12 Abnormality images obtained from a inspection area I and b inspection area II, and final images obtained from c inspection area I and d inspec-tion area II of the 10 kW GFRP wind turbine blade


1 3

8.19% with respect to the actual size. Since delamination was caused by inserting a Teflon tape between layers, the equivalent delamination area would be larger than the size of the inserted Teflon tape. Therefore, it is speculated that the error in measurement of an actual delamination will be comparatively less.

4.4 Remote Inspection

Figure 13 shows a test setup for remote inspection of the 10 kW GFRP wind turbine blade. The stand-off distance from the target 10 kW GFRP wind turbine blade to the gal-vanomotorized scanner and IR camera were set to 10 m. Note that, since the stand-off distance was ten times the pre-vious laboratory test distance, the spatial resolution of the IR camera was reduced from 390 to 3.9 mm (10 times). For comparison with the previous test, the same blade area as tested in Sect. 4.3 was inspected. Note that, the laser inten-sity and scanning speed were same as the tests described in Sect. 4.3. The two delamination defects with 10 mm and

20 mm diameter in the inspection area II were successfully visualized and no false alarm is caused by surface damage as depicted in Fig. 14d. However, due to the reduced spatial resolution of the IR camera, the number of pixels represent-ing the delamination area in the inspection area II was only 20 pixels, and the corresponding physical size is estimated to 319.41 mm2 which depicts an error rate of 17.30% with respect to the actual size.

5 Full‑Scale Tests

A 3 MW GFRP wind turbine blade manufactured by the Wind Turbine Technology Research Center at Korea Insti-tute of Materials Science (KIMS) was used in full-scale tests to validate the performance of the proposed CLLST system, as illustrated in Fig. 15. In this test, real delamination was produced on the wind turbine blade by fatigue testing, rather than by inserting a piece of Teflon tape.

Fig. 13 Remote inspection test setup

Fig. 14 Abnormality images obtained from a remote (10 m) inspection of a inspection area I and b inspection area II, and final images obtained from c inspection area I and d inspec-tion area II of the 10 kW GFRP wind turbine blade


1 3

5.1 Description Of The 3 MW GFRP Wind Turbine Blade

A 3 MW GFRP wind turbine blade, which has dimensions of approximately 50, 000 × 2000 × 200mm3 was tested, as depicted in Fig. 15. The 3 MW GFRP wind turbine blade was constructed by laminating with glass fiber plies, wood plates, and resin. Note that the details of the material com-pounds and the design parameters were unknown to the authors due to the trade secrets that were held by the manu-facturer. The 3 MW GFRP wind turbine blade was mounted on a fixture that cantilevered it horizontally, as depicted in Fig. 15a, and dual-axis resonance fatigue tests are per-formed, in accordance with the IEC 61400-23 standard: 510,000 cycles under an equivalent amplitude of 5352 kNm and a mean of 5970 kNm for flap-wise structure and 780,000 cycles under an equivalent amplitude of 4454 kNm and a mean of 0 kNm for edge-wise structure, at the end of the blade root at a frequency ranging from 0.45 to 0.7 Hz [39]. During the fatigue tests, two main types of damage were produced: (1) as for Type I damage, fiber separation and propagation of delamination along the fibers inside the lami-nates were created by the tension load, and (2) as for Type II damage, delamination between laminates are produced by large moments at the base of the wind turbine blade [40]. Figure 16b illustrates the Type I damage produced during the cyclic loading tests.

5.2 Test Setup

Figure 15 illustrates the test setup to perform the full-scale test. In this full-scale test, a laser intensity of 18.75 mW∕mm2 and a scanning speed of 15.8 mm∕s were used for the line laser beam excitation. The size of the line beam laser was approximately observed to be 800 mm long and 1 mm wide. The distance between the target structure and IR camera was observed to be approximately 5500 mm, whereas the distance between the target structure and gal-vanomotorized scanner was observed to be approximately 6000 mm. The size of each inspection area was 7600 cm2 , and each inspection required 60 s. The corresponding

Fig. 15 Inspection of the 3 MW GFRP wind turbine blade using the continuous line laser scanning thermography system: a 3 MW GFRP wind turbine blade, b overall configuration of the CLLST system, and c close-up view of the CLLST system

Fig. 16 Inspection areas on the 3 MW GFRP wind turbine blade: a inspection area I without damage and b inspection area II with delamination


1 3

inspection speed is 126.4 cm2∕s . Inspection area I depicted in Fig. 16a does not include delamination, whereas the inspection area II in Fig. 16b includes delamination.

5.3 Test Results

Figure 17a, b illustrate the abnormality images contain-ing the noise components. As expected, the noise compo-nents are successfully eliminated in the final images that are depicted in Fig. 17c, d. For inspection area II that is depicted in Fig. 17d, type I damage, i.e. delamination that is produced along the tension line is successfully imaged. Additionally, no false alarm is produced in the inspection area I depicted in Fig. 17c. Note that the blade surface is curved, but the proposed instantaneous delamination visu-alization algorithm successfully eliminates the temperature gradient that is produced by the curved surface to visualize the delamination. Furthermore, these results exhibit that the system functions up to a range of 5 m for the inspection.

6 Conclusion

In this study, a continuous line laser scanning thermography (CLLST) system and an instantaneous delamination visu-alization algorithm are proposed to detect internal delami-nation in the wind turbine blades. The CLLST system can remotely generate and measure thermal waves present in a target structure by continuously scanning using a line

laser beam and an IR camera, respectively. The measured thermal responses are automatically analyzed to visualize the delamination. The CLLST system can rapidly inspect the target blades and visualize internal delamination in a fully noncontact and autonomous manner. Furthermore, the proposed delamination visualization algorithm successfully extracts delamination without any false alarm caused by the surface patterns and scratches on the target blade surface. The performance of the CLLST system was experimentally validated through the laboratory-scale tests of a CFRP plate and a 10 kW GFRP wind turbine blade, and the perfor-mance of the remote inspection was also validated through the experiment. A full-scale test using the 3 MW GFRP wind turbine blade demonstrates the potential of the system to be applied to for remote and large-area inspections. In detail, 10 mm diameter delamination that was located 1 mm underneath the structures can be successfully visualized even 10 m far from the target blade with a scanning speed of 20 mm∕s . The sensing range can be further improved by adjusting the intensity of the laser beam and angle of view of the IR camera.

Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea gov-ernment (MSIT) (No. 2019R1A3B3067987).

References

1. Gay, D. (2014). Composite materials: design and applications. Boca Raton: CRC Press. ISBN: 9780429101038.

Fig. 17 Abnormality images obtained from a inspection area I and b inspection area II, and final images obtained from c inspection area I and d inspec-tion area II of the 3 MW GFRP wind turbine blade


1 3

2. Campbell, F. C. (2010). Structural composite materials. Materials Park: ASM International. ISBN: 9781615030378.

3. Aslan, Z., & Daricik, F. (2016). Effects of multiple delaminations on the compressive, tensile, flexural, and buckling behaviour of E-glass/epoxy composites. Composites Part B: Engineering, 100, 186–196.

4. Zabala, H., Aretxabaleta, L., Castillo, G., Urien, J., & Aurre-koetxea, J. (2014). Impact velocity effect on the delamination of woven carbon–epoxy plates subjected to low-velocity equi-energetic impact loads. Composites Science and Technology, 94, 48–53.

5. Tao, C., Qiu, J., Yao, W., & Ji, H. (2016). A novel method for fatigue delamination simulation in composite laminates. Com-posites Science and Technology, 128, 104–115.

6. Pagano, N. J. (2012). Interlaminar response of composite materi-als (Vol. 5). North Holland: Elsevier. ISBN: 9780444597205.

7. Li, D., Ho, S.-C. M., Song, G., Ren, L., & Li, H. (2015). A review of damage detection methods for wind turbine blades. Smart Materials and Structures, 24(3), 033001.

8. Liu, W., Tang, B., Han, J., Lu, X., Hu, N., & He, Z. (2015). The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews, 44, 466–472.

9. Roh, H. D., Lee, H., & Park, Y.-B. (2016). Structural health mon-itoring of carbon-material-reinforced polymers using electrical resistance measurement. International Journal of Precision Engi-neering and Manufacturing-Green Technology, 3(3), 311–321.

10. Ryu, C.-H., Park, S.-H., Kim, D.-H., Jhang, K.-Y., & Kim, H.-S. (2016). Nondestructive evaluation of hidden multi-delamination in a glass-fiber-reinforced plastic composite using terahertz spec-troscopy. Composite Structures, 156, 338–347.

11. Wang, J., Zhang, J., Chang, T., & Cui, H.-L. (2019). A Compara-tive Study of Non-destructive Evaluation of Glass Fiber Rein-forced Polymer Composites Using Terahertz, X-ray, and Ultra-sound Imaging. International Journal of Precision Engineering and Manufacturing, 20(6), 963–972.

12. Liu, Z., Yu, H., Fan, J., Hu, Y., He, C., & Wu, B. (2015). Baseline-free delamination inspection in composite plates by synthesiz-ing non-contact air-coupled Lamb wave scan method and virtual time reversal algorithm. Smart Materials and Structures, 24(4), 045014.

13. Karabutov, A., & Podymova, N. (2014). Quantitative analysis of the influence of voids and delaminations on acoustic attenuation in CFRP composites by the laser-ultrasonic spectroscopy method. Composites Part B: Engineering, 56, 238–244.

14. Park, B., An, Y.-K., & Sohn, H. (2014). Visualization of hidden delamination and debonding in composites through noncontact laser ultrasonic scanning. Composites science and Technology, 100, 10–18.

15. Park, B., Sohn, H., Malinowski, P., & Ostachowicz, W. (2017). Delamination localization in wind turbine blades based on adap-tive time-of-flight analysis of noncontact laser ultrasonic signals. Nondestructive Testing and Evaluation, 32(1), 1–20.

16. Sohn, H., Dutta, D., Yang, J., DeSimio, M., Olson, S., & Swen-son, E. (2011). Automated detection of delamination and disbond from wavefield images obtained using a scanning laser vibrometer. Smart Materials and Structures, 20(4), 045017.

17. An, Y.-K., Park, B., & Sohn, H. (2013). Complete noncontact laser ultrasonic imaging for automated crack visualization in a plate. Smart Materials and Structures, 22(2), 025022.

18. Hsu, D. K., Lee, K.-S., Park, J.-W., Woo, Y.-D., & Im, K.-H. (2012). NDE inspection of terahertz waves in wind turbine com-posites. International Journal of Precision Engineering and Manufacturing, 13(7), 1183–1189.

19. Park, J.-W., Im, K.-H., Yang, I.-Y., Kim, S.-K., Kang, S.-J., Cho, Y.-T., et al. (2014). Terahertz radiation NDE of composite

materials for wind turbine applications. International Journal of Precision Engineering and Manufacturing, 15(6), 1247–1254.

20. Kim, D.-H., Ryu, C.-H., Park, S.-H., & Kim, H.-S. (2017). Non-destructive evaluation of hidden damages in glass fiber rein-forced plastic by using the terahertz spectroscopy. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(2), 211–219.

21. Schilling, P. J., Karedla, B. R., Tatiparthi, A. K., Verges, M. A., & Herrington, P. D. (2005). X-ray computed microtomography of internal damage in fiber reinforced polymer matrix compos-ites. Composites Science and Technology, 65(14), 2071–2078.

22. Tan, K. T., Watanabe, N., & Iwahori, Y. (2011). X-ray radiog-raphy and micro-computed tomography examination of damage characteristics in stitched composites subjected to impact load-ing. Composites Part B: Engineering, 42(4), 874–884.

23. Maldague, X. P. (2002). Introduction to NDT by active infrared thermography. Materials Evaluation, 60(9), 1060–1073.

24. Lisle, T., Bouvet, C., Hongkarnjanakul, N., Pastor, M.-L., Rival-lant, S., & Margueres, P. (2015). Measure of fracture toughness of compressive fiber failure in composite structures using infra-red thermography. Composites Science and Technology, 112, 22–33.

25. Foudazi, A., Edwards, C. A., Ghasr, M. T., & Donnell, K. M. (2016). Active microwave thermography for defect detection of CFRP-strengthened cement-based materials. IEEE Transactions on Instrumentation and Measurement, 65(11), 2612–2620.

26. He, Y., Tian, G., Pan, M., & Chen, D. (2014). Impact evaluation in carbon fiber reinforced plastic (CFRP) laminates using eddy current pulsed thermography. Composite Structures, 109, 1–7.

27. Tang, Q., Dai, J., Bu, C., Qi, L., & Li, D. (2016). Experimental study on debonding defects detection in thermal barrier coating structure using infrared lock-in thermographic technique. Applied Thermal Engineering, 107, 463–468.

28. Doroshtnasir, M., Worzewski, T., Krankenhagen, R., & Röllig, M. (2016). On-site inspection of potential defects in wind turbine rotor blades with thermography. Wind Energy, 19(8), 1407–1422.

29. Ranjit, S., Kang, K., & Kim, W. (2015). Investigation of lock-in infrared thermography for evaluation of subsurface defects size and depth. International Journal of Precision Engineering and Manufacturing, 16(11), 2255–2264.

30. Kim, G., Hong, S., Kim, G. H., & Jhang, K.-Y. (2012). Evaluation of subsurface defects in fiber glass composite plate using lock-in technique. International Journal of Precision Engineering and Manufacturing, 13(4), 465–470.

31. Moran, J., & Rajic, N. (2019). Remote line scan thermography for the rapid inspection of composite impact damage. Composite structures, 208, 442–453.

32. Li, T., Almond, D. P., & Rees, D. A. S. (2011). Crack imaging by scanning pulsed laser spot thermography. NDT & E International, 44(2), 216–225.

33. Li, T., Almond, D. P., & Rees, D. A. S. (2011). Crack imaging by scanning laser-line thermography and laser-spot thermography. Measurement Science and Technology, 22(3), 035701.

34. An, Y.-K., Yang, J., Hwang, S., & Sohn, H. (2015). Line laser lock-in thermography for instantaneous imaging of cracks in semi-conductor chips. Optics and Lasers in Engineering, 73, 128–136.

35. Peeters, J., Ibarra-Castanedo, C., Khodayar, F., Mokhtari, Y., Sfarra, S., Zhang, H., et al. (2018). Optimised dynamic line scan thermographic detection of CFRP inserts using FE updating and POD analysis. Ndt & E International, 93, 141–149.

36. Honner, M., Honnerova, P., Kučera, M., & Martan, J. (2016). Laser scanning heating method for high-temperature spectral emissivity analyses. Applied Thermal Engineering, 94, 76–81.

37. Kreith, F., Manglik, R. M., & Bohn, M. S. (2012). Princi-ples of heat transfer. Stamford: Cengage learning. ISBN: 9781439061862.


1 3

38. https ://www.skche mical s.com/busin ess/sf_pop.do?no=1. Accessed 1 October 2019.

39. Lee, H. G., Kang, M. G., & Park, J. (2015). Fatigue failure of a composite wind turbine blade at its root end. Composite Struc-tures, 133, 878–885.

40. Pascoe, J., Alderliesten, R., & Benedictus, R. (2013). Methods for the prediction of fatigue delamination growth in composites and adhesive bonds—a critical review. Engineering Fracture Mechan-ics, 112, 72–96.

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Soonkyu Hwang received the B.E. degree in Architectural Engineering from Chungnam National University, Daejeon, South Korea in 2013, and M.E. degree in Civil and Enviromental Engineering (CEE) from Korea Advanced Institute of Science and Technology (KAIST), in 2016. Currently, he is working toward the Ph.D. degree in CEE from KAIST. His research inter-ests include non-destructive test-ing (NDT), structural health monitor ing (SHM), ther-m o g r p a h y, a n d i m a g e

processing.

Yun‑Kyu An received the B.E. degree from Korea University, Seoul, South Korea in 2007, and the M.E. and Ph.D. degrees from KAIST, Daejeon, South Korea, in 2009 and 2013, respectively, all in Civil and Enviromental Engineering (CEE). He joined the faculty of CEE Department, Southeast University, Nanjing, China from 2014 to 2015. Cur-rently, he is with the department of Architectural Engineering at Sejong University as an associ-ate processor. His research inter-ests include in the areas of non-

destructive testing (NDT), machine learning and data processing.

Jinyeol Yang received the B.E., M.E., and Ph.D degrees from KAIST, Daejeon, Korea, in 2010, 2011, and 2016, respec-tively, all in Civil and Enviro-mental Engineering (CEE). Cur-rently, he is with the Test and Package Center at Samsung Electronics as senior researcher. His research interests include metrology and inspection, non-destructive testing (NDT), struc-tural health monitoring (SHM), ultrasonics, thermogrpahy, and signal processing.

Hoon Sohn received the B.E. and M.E. degrees from Seoul National University, Seoul, Korea, and the Ph.D. degree from Stanford University, Stan-ford, CA, in 1992, 1994, and 1999, respectively, all in Civil and Enviromental Engineering (CEE). He joined Los Alamos National Laboratory (LANL) as a director-funded postdoctoral fellow from 1999 to 2001, and he was a technical staff member from 2001 to 2004. He joined the faculty of the CEE Department, Carnegie Mellon University,

Pittsburgh, PA, in August 2004. Currently, he is with the department of CEE at KAIST as a full professor. For the last fifteen years, his research interest has been in the area of structural health monitoring and sensing technologies.

https://www.skchemicals.com/business/sf_pop.do?no=1

remote inspection of internal delamination in wind turbine ... · remote inspection of internal...

Documents