2.1. Blade test setup

For the present test campaign, a 52-meter wind turbine blade is used. The blade was manufactured from glass fiber reinforced epoxy resin with balsa and plywood core materials. Resin infusion was performed in a single shot through the IntegralBlades technology (see Stiesdal et al., Reference Stiesdal, Enevoldsen, Johansen, Kristensen, Nörtem and Winther-Jensen2003). The blade is mounted pressure side (PS) up on an indoor test stand (see Figure 1). The blade test is performed at the Siemens Gamesa Blade Test Center in Aalborg, Denmark. The test hall blocks out wind and is temperature regulated, which helps in limiting the influence of variations in environmental conditions on the state of the wind turbine blade and of the SHM systems. The ambient temperature inside the hall is maintained between 25 and 26°C for the majority of the time. It is, however, not part of this study to investigate active compensation of environmental and temperature effects. Thus, environmental and temperature effects are desired to be filtered out passively, which is done by conducting the test inside the test hall.

Figure 1. A 52-meter blade on test stand at the Siemens Gamesa Blade Test Center in Aalborg, Denmark. The blade is mounted pressure side up for flapwise fatigue testing. The image shows the blade without the fatigue exciter mounted.

In the presented test campaign, both flapwise and edgewise fatigue testing are conducted in sequence. Thus, two different fatigue exciters are mounted on the blade at different points in time during the test campaign. Multiple types of sensing systems are used: SGs, FBGs, a DIC system, AE sensors, accelerometers, an active vibration monitoring system, and a GW pitch–catch system. Some of the sensing systems are collecting data continuously during fatigue testing, stand-still, and flapwise hand-excitation of the blade tip, whereas others collect data periodically. An overview of fatigue test uptime, damage introductions, and repairs is shown in Figure 2. Four different damage cases are investigated over the course of the test campaign. The normalized strain range, averaged over 10-minute intervals, is plotted in Figure 2. The signal is sampled from an SG at spanwise position 34 m, placed on the PS, measuring flapwise normal strain. Some changes and fluctuations in the applied load can be observed. The applied flapwise load is increased by 10% on October 29 and by an additional 10% on December 9, 2020. Fluctuations in the applied load were caused by issues with the flapwise fatigue exciter, which were ultimately fixed.

Figure 2. Flapwise normal strain range during test campaign, showing time of damage introduction (Dam1–Dam4), repair (Rep1 and Rep2), and test uptime. Testing with damage #1, damage #2, and damage #3 (Dam1, Dam2, and Dam3, respectively) is performed in flapwise fatigue, whereas testing with damage #4 (Dam4) is performed in edgewise fatigue. Due to the SG measuring flapwise normal strain, the strain range is lower during edgewise fatigue testing, compared to flapwise fatigue testing.

2.2. Damage cases

The considered damage cases are based on damages observed in literature on blade tests. Common damage types in wind turbine blades were reviewed by Sørensen et al. (Reference Sørensen, Jørgensen, Debel, Jensen, Jensen, Jacobsen and Halling2004), with the presented damage types being based on observations from quasi-static testing of a wind turbine blade. These include the following: adhesive bondline damage, sandwich face/core debonding, delamination, cracking along the fiber direction, and buckling damage. Due to the use of the IntegralBlades technology for the manufacturing of the blade used in this work, there are no bondlines between the PS and SS shells. Thus, bondline damages are not relevant for this blade.

For this work, cracks transverse to the spanwise direction, located in the blade shell and blade web, are studied. Transverse cracks may initiate from manufacturing defects such as transverse wrinkles, as investigated by Leong et al. (Reference Leong, Overgaard, Thomsen, Lund and Daniel2012). Artificial cracks are induced in the 52-meter blade by use of a small angle grinder, cutting through the thickness of the laminate. A total of four damage cases are investigated, denominated damage #1–#4, with their global position sketched on the blade in Figure 3. Images of the damages are shown in Figure 4. Descriptions of the damage cases, their global spanwise position, and the manual lengths induced are presented in Table 2. Damage #3 is selected for analysis in this paper, since progressive damage development was obtained continuously after manual introduction of the damage. The damage is initiated as a transverse crack in both the PS and SS parts of the web start, as will be elaborated in Section 3.

Figure 3. Spanwise and edgewise placements of investigated damage cases, including global unidirectional SG and triaxial accelerometer placement. All dimensions in meter. At positions marked with SGs, SGs are placed on the suction side, pressure side, leading edge, and trailing edge, respectively.

Figure 4. Investigated damage cases. #1: Transverse crack in pressure side shell. #2: Transverse crack in web start. #3: Transverse crack in web start. #4: Transverse/longitudinal crack in leading edge shell.

Table 2. Overview of the investigated damage cases.

Abbreviations: LE, leading edge; PS, pressure side; TE, trailing edge.

2.3. Sensing systems

The main objective of the presented blade test is to use various sensing systems to record data for healthy and damaged states of the blade. These data can then also serve as a basis for future studies on SHM in wind turbine blades, with the purpose of investigating the opportunities and limitations of the different sensing systems for damage detection in large wind turbine blades. The used sensing systems, listed in Table 3, include pointwise sensors (such as SGs), local damage detection systems (such as AE sensors), and global damage detection systems (such as a series of distributed accelerometers combined with subsequent data analysis). Local damage detection systems are useful for detection of damages within a limited area, for example, within a radius of 1 m from each sensor in a cluster of sensors. A global damage detection system should, ideally, facilitate detection of damages in the entire blade. In addition, to the listed sensing systems, a digital camera was placed inside the blade to enable surveillance of the damages during testing.

Table 3. Overview of the used sensing systems.

2.3.1. Strain sensors

The load level in wind turbine blades during fatigue testing is commonly evaluated by use of SGs. In the present blade test, various SGs are distributed along the span of the blade. Unidirectional SGs are placed on the PS, SS, leading edge (LE), and TE at multiple spanwise positions between 1 and 40 m, as shown in Figure 3. In addition, SGs are placed locally near the positions of damage #1–#3. An SG placed near damage #3 is shown in Figure 5. For this damage, strain rosettes are used to measure in-plane strain in multiple directions near the damage. The type of unidirectional SG used is a Vishay WD-DY-375BG-350, and the type of strain rosettes used is a Tokyo Measuring Instruments Lab. FRAB-10-11. A custom measurement system in accordance with IEC 61400-23 and ISO 17025 is used.

Figure 5. Sensing systems near damage #3. DIC system 1: Black-and-white speckle pattern; 2: Digital camera; 3: Tripod fixture with LED lights. 4: Strain rosette on the web start near damage #3.

As an addition to the SGs, FBGs are installed at multiple positions near the conventional foil gauges to measure spanwise normal strain. Thus, the performance of the two different strain sensors may be compared over the course of the fatigue testing campaign.

The local SGs are placed to monitor progression of the respective damages over time, which will have an influence on the local strain distribution. The strain magnitudes near crack tips will increase, whereas strain magnitudes near free crack faces will decrease. Thus, information about the location and size of a damage can also be inferred, if a sufficient number of strain sensors are located near a damage.

2.3.3. Acoustic emission

An AE system is used to continuously monitor AE activity close to the damages during fatigue testing. A Vallen AMSY-6 system with four VS900 piezoelectric AE sensors is utilized. Figure 6a shows AE sensors placed near damage #3. AE sensors measure high-frequency vibrations, with the utilized system capturing vibrations within a range between 100 and 900 kHz. Using the AE data from the test, damage propagation may be indicated by, for example, increased AE hit count or an increase in intensity of the AE hits. Damage initiation and propagation may thus be inferred directly from the recorded data. AE data are recorded continuously during the test, with all hits below 40 dB being filtered out. The filtering removes a large part of noisy scatter from the fatigue test, which would otherwise be counted as false-positive hits.

Figure 6. A: Acoustic emission (AE) sensor setup; 1: AE sensor; 2: Magnetic clamping fixture used to fix the AE sensor to the blade; 3: Steel mounting plate adhered to the blade laminate. B: Active vibration monitoring system; 1: Mounting plates adhered to the blade laminate; 2: Accelerometer; 3: Force transducer; 4: Vibration shaker.

2.3.5. Active vibration monitoring system

An active vibration monitoring system, using accelerometers and an electrodynamical vibration shaker, is designed by the authors and tested in connection with the blade test. The electrodynamical shaker emits vibrations at high frequencies relative to, for example, the first 20 eigenfrequencies of the blade. The vibration response is recorded by a number of accelerometers, distributed within up to 15 m of the vibration shaker. The active vibration monitoring system consists of 11 Brüel & Kjær type 4507-B-006 piezoelectric uniaxial accelerometers with a frequency range of 0.2–6,000 Hz. The electrodynamical shaker is a DEWESoft model DS-IS-40, outputting up to 40 N between 10 and 3,000 Hz. A colocated Dytran 1053V1 force transducer is added to the mounting of the vibration shaker to allow measurement of the input force. An NI cDAQ-9185 data acquisition unit is used with three NI-9234 input modules and an NI-9260 output module. Data acquisition is controlled via a laptop PC with MATLAB software. With the electrodynamical shaker, three different types of vibration signals are applied to the blade: sinusoidal burst, exponential chirp, and white Gaussian noise. The accelerometer signals are sampled at 25,600 Hz over a period of 14 s, with the active vibration input being applied 1 s after sampling begins and ended 1 s before sampling stops. See Figure 7 for an example of an applied exponential chirp signal with a frequency range of 100–3,000 Hz.

Figure 7. Chirp signal generated with electrodynamical shaker for damage #3. A: Time series of force input by the vibration shaker measured with a force transducer (see Figure 6b). B: Spectrogram of force time series in A. C: Time series of acceleration signal measured with accelerometer on the web 1 m away from shaker. D: Spectrogram of acceleration time series in C.

Similarly to the previously described accelerometer-based system, the active vibration monitoring system enables damage detection based on changes in the frequency response of the blade within the range of the applied active vibrations. By using an active input of vibrations with a higher frequency than, for example, the first 20 natural frequencies of the blade, the wavelengths of the vibration inputs are smaller, which means that smaller damages can be detected.

The active vibration monitoring system is similar to a system developed by Tcherniak and Mølgaard (Reference Tcherniak and Mølgaard2015), which incorporates an electromechanical hammer and a number of distributed accelerometers. The main difference between the system designed by Tcherniak and Mølgaard (Reference Tcherniak and Mølgaard2015) and the present active vibration system lies in the source of the active vibration input. Tcherniak and Mølgaard (Reference Tcherniak and Mølgaard2015) used an electromechanical actuator, which provides a high-amplitude plunger hit that excites a wide frequency range for a short time period. The present work uses an electrodynamical shaker, where the vibration frequency, amplitude, and time period of excitation can be controlled by the user through computer software.

2.3.6. Guided waves

A GW pitch–catch system, consisting of two piezoelectric actuators and two piezoelectric sensor strips with a total of 16 individual sensors, is placed around the damages to monitor the local changes caused by initiation and propagation of the damages. The GW system installed near damage #3 is shown in Figure 8a. The sensor patches are placed at spanwise distances between 0.25 and 0.35 m from the investigated damages, and the actuators are placed at spanwise distances of 0.5 m from the damages. Initiation and propagation of damage can be detected through, for example, changes in amplitude and reflections of waves (see Guan et al., Reference Guan, Lu, Duan and Wang2017). The GW system has been designed by the Dynamics Research Group at The University of Sheffield.

Figure 8. A: Web start toward suction side (SS) with GW pitch–catch system installed near damage #3; 1: GW actuator; 2: GW sensor patch with eight individual sensors. B: Damage #3 on the SS part of the web start after the test was stopped with this damage case. The damage was initially introduced through grinding of the laminate on February 5, 2021. A delamination formed at the crack tip, but it propagated significantly slower than the damage on the pressure side part of the web start.

2.4. Blade finite element model

An FE model of the blade is used to perform studies on the effect of the damages on the strain distribution as well as on the modal parameters. The FE model is discretized using linear shell elements, and cracks in the laminate are modeled through nodal separation. Regarding boundary conditions, the blade is fixed at the root, and distributed loads, which correspond to the fatigue loading applied to the blade during fatigue testing, are applied to the FE model. To facilitate the use of a smaller element size around modeled damages, a submodel of the geometry in the vicinity of damages is created. At the boundary of the submodel, displacements from nodes at the same location, extracted from the global blade model, are applied. See Fremmelev et al. (Reference Fremmelev, Ladpli, Bernhammer, McGugan and Branner2020) for further details regarding the blade FE model as well as the approach used for damage modeling. The FE model is used to estimate strain values for comparison with SG measurement. The calculated eigenfrequencies are used for comparison with eigenfrequencies estimated in the blade test through OMA, which is elaborated in Section 4.

4.1. Strain gauge measurements

The measurements from four SGs placed near the position of damage #3 are investigated. The SGs are placed at spanwise positions 6.3, 6.5, 6.55, and 8.5 m, and are denominated SG6.3, SG6.5, SG6.55, and SG8.5, respectively, in the following. The SGs at spanwise positions 6.5 and 6.55 m are shown in Figure 11a. Figure 11b shows the normalized in-plane shear strain ranges $ \Delta {\varepsilon}_{12,\operatorname{norm}} $ measured by the SGs on the web laminate near damage #3, as well as the normalized in-plan normal strain range $ \Delta {\varepsilon}_{11,\operatorname{norm}} $ measured by an SG at spanwise position 34 m. SG6.3 is located halfway down the web start toward the PS; SG6.5 on the spar cap, 100 mm from the web; SG6.55 on the web next to the damage, halfway down the web start toward the PS; SG8.5 on the web at approximately the same height as SG6.3 and SG6.55. Measurements from SG34, placed on the PS spar cap, are included as a reference for the load level, which is steady around its nominal value during fatigue testing, thus not indicating any changes as a result of damage #3. Dotted vertical lines in Figure 11b with letters A–D refer to the images of damage #3 in Figure 10. The vertical axis in Figure 11b has been limited to 3 for better visualization of the changes apparent in SG6.3, SG6.5, and SG8.5. SG6.55 exhibits a sharp peak of approximately 6.5 times the nominal strain range, occurring on February 26, 2021, coinciding with the dotted line B. This peak drops significantly around February 28, 2021. The normalized in-plane shear strain range $ \Delta {\varepsilon}_{12,\operatorname{norm}} $ for each individual SG is calculated as shown in Equation (1), and the spanwise normal strain range $ \Delta {\varepsilon}_{11,\operatorname{norm}} $ is calculated in the same manner using spanwise normal strain measurements.

(1) $$ \Delta {\varepsilon}_{12,\operatorname{norm}}=\frac{\varepsilon_{12,\max }-{\varepsilon}_{12,\min }}{2}\frac{1}{\Delta {\varepsilon}_{12,\mathrm{nom}}}, $$

where $ {\varepsilon}_{12,\max } $ and $ {\varepsilon}_{12,\min } $ are the maximum and minimum values of the in-plane shear strain during fatigue testing, respectively, and $ \Delta {\varepsilon}_{12,\mathrm{nom}} $ is the nominal shear strain range, measured in the healthy state of the blade.

Figure 11. (a): Acoustic emission (AE) and strain gauge placement near damage #3. The location of the damage is marked with a red rectangle. (b): Normalized strain ranges $ \Delta {\varepsilon}_{12,\operatorname{norm}} $ (SG6.3, SG6.5, SG6.55, and SG8.5) and $ \Delta {\varepsilon}_{11,\operatorname{norm}} $ (SG34) for testing with damage #3. The damage was manually introduced on February 22, 2021, coinciding with the dotted line A. (c) Sum of AE hits for testing with damage #3. The four AE sensors are denominated AE1–AE4. AE measurements for the last few days of testing with this damage are not available.

As described in Section 3, damage #3 was initially introduced through grinding of the web laminate, from which a visible crack did, however, not develop during fatigue testing. The strain range of SG6.55, the yellow curve in Figure 11b, shows a slight increase in the period from February 13, 2021 to February 18, 2021, which was after introduction of the grinding, but before introduction of the laminate crack. This may indicate that some damage propagation occurred after introduction of the grinding in the web laminate. However, the damage propagation was slow and not visible during inspection. Thus, it was decided to introduce the crack manually by cutting through the laminate, which was done on February 22, 2021.

Significant changes in magnitude of all strain ranges, except for that of SG34, included in Figure 11b can be seen starting around February 27, 2021, where fatigue testing is started after manual introduction of the damage in the form of a laminate crack. SG6.55, which is placed next to the damage, shows a large increase in the strain range as a consequence of the damage being introduced. The increase in strain range is caused by the strain concentration occurring at the crack tip, which is located at approximately the same height of the web as SG6.55. The initial changes in the strain range of the other SGs in Figure 11b are minor compared to the changes from SG6.55. The large increase in strain range of SG6.55 is followed by a large drop in the strain range as the damage propagates. These changes in the magnitude of the strain range are explained by the crack tip growing past the position of the SG, and loads being redistributed around the damage. This leads to reduced strains in the laminate near the free crack faces, where SG6.55 is located. The observed changes in the strain measurements are similar to trends observed by the authors in Fremmelev et al. (Reference Fremmelev, Ladpli, Bernhammer, McGugan and Branner2020), where numerical studies on the strain distribution near a laminate crack in a blade were studied. In the cited work, it was observed that strain magnitudes would increase as a crack grew toward the location of a strain sensor, whereas the strain magnitude would decrease once the crack tip had grown past the location of the strain sensor. This behavior was proposed to be used for the estimation of crack length and damage location.

SG6.5, which is placed on the spar cap, shows a gradual decrease in the strain range after introduction of the damage. This indicates that a reduced amount of the shear loads is transferred between the spar cap laminate and the web laminate at the spanwise position of the damage. SG6.3, which is placed on the web start, shows a large drop in the strain range after introduction of the damage. With increasing damage propagation, the strain range increases to more than twice the nominal value. For SG8.5, the strain range shows a continuous increase in magnitude after introduction of the damage and with further damage propagation. The measurement results from SG8.5 indicate that a damage in the web can be detected within a spanwise distance of at least 2 m using strain measurements.

Figure 12 shows the FE geometry of the web start, overlaying the FE results for the difference in shear strain range caused by the inclusion of damage #3 with lengths of 100 mm in both the PS and SS web starts. For reference on the geometry, see Figures 9 and 10, which show photographs of the web start. The normalized shear strain range is calculated in the same manner as for the SG measurements, shown in Equation (1). Figure 12 illustrates the distance at which the strain distribution is affected by the inclusion of damage #3. The plot shows changes in the strain distribution within a distance of approximately 0.5 m with the initial length of the manually induced damage.

Figure 12. Difference in in-plane shear strain range at the web start between blade with the inclusion of damage #3 and the healthy blade. The damage is included at spanwise position 6.5 m with lengths of 100 mm on both the pressure side and the suction side. The color scale is limited between 1 and −1 to make changes in strain further away from the damage visible. Thus, changes in the strain range in the plot correspond to between 100 and −100% of the nominal strain range, whereas the strain range gradients of the peaks beyond this range are not readable from the plot.

Using the FE model of the blade, strain ranges at the locations of the SGs are calculated with damage lengths on the PS web between 0 mm, that is, the healthy state of the blade, and 250 mm, which corresponds to the height of the web start on the PS at spanwise location 6.5 m. The strain range results, extracted at the same positions as those of the SGs presented in Figure 11b, are plotted in Figure 13. Only increasing damage length on the PS of the web is studied, since this is the location where the damage propagated during fatigue testing. Comparing the FE strain range in Figure 13 to the SG measurements in Figure 11b, the following observations are made for the four SG positions:

• • SG6.3: The SG measurements show values of magnitudes lower than the nominal strain range after introduction of the damage with a length of 100 mm. By comparison, the FE model yields a large increase in strain range after introduction of the damage. Toward the maximum damage length, the SG measurements reach their largest value, whereas the strain range from the finite element analysis (FEA) decreases below the nominal value.

• • SG6.5: Both SG measurements and FEA results show similar trends with a decrease in the strain range, followed by an increase in strain range.

• • SG6.55: The large changes at SG6.55 in the FEA are due to a singularity at the crack tip, where strains approach infinity with decreasing element size. This is not physical behavior, and indicates that the measurement in the model is too close to the crack tip, and that why the magnitudes of the measurements are not accurate and comparable to SG measurements from the blade test. Considering the general trends of the FE strain range, it is very similar to the SG measurements. Both SG measurements and FEA results show a sharp increase in the strain range after introduction of the damage, followed by a slight decrease, and again followed by an increase in the strain range. For the SG measurement, a final decrease below the nominal strain range is observed (not visible in the plot due to axis limits).

• • SG8.5: There is a large difference in the magnitude of changes in the strain range with increasing crack length. The model predicts an increase in the strain range of 7% with a damage length through the whole web height, whereas the SG measurements show an increase over 70%. The model predicted a change in the spanwise normal strain range comparable to the SG measurements of the shear strain range.

Figure 13. Normalized strain range plotted as a function of the crack length in the pressure side part of damage #3. The second axis is limited to six times the nominal strain range to cut out the peak from spanwise position 6.55 m, which is located very close to the stress concentration and will thus tend toward a nonphysically large value.

Inaccuracies of the FE model are deemed to be due to the model being relatively simple compared to, for example, a model with 3D solid elements with a fracture mechanics model for the crack. In general, although the FE model is not able to accurately approximate strain ranges for all the SGs, it is able to predict general trends in the changes occurring in some of the SG measurements due to damage initiation and progression.

4.2. Acoustic emission system

Four AE sensors are placed between 0.2 and 0.5 m from the point of damage initiation (see Figure 11a). AE data recorded during the test period for damage #3 are shown in Figure 11c, with the sum of AE hits plotted along the vertical axis with logarithmic axis scaling. Damage propagation during fatigue testing is inferred from the sensor signals starting around February 27, 2021, coinciding with the dotted vertical line B, where large changes in the magnitude of the strain range are measured, and high increases in the number of AE hits are recorded. Jumps in AE hit count prior to February 27, 2021 are mainly attributed to measurement noise captured during fatigue testing. Starting February 27, 2021, jumps in AE hit count are attributed to damage propagation, for example, sensor AE4 showing a large jump just prior to March 7, 2021. At this point, the damage has grown toward the spar cap, and the sensors placed on the spar cap measure AE hits as the damage propagates further.

From the start of the fatigue test around February 10, 2021, an increase in the AE hit count is visible in Figure 11c. In general, the trends from both the AE and SG measurements in Figure 11b,c, respectively, indicate damage propagation, starting from the point of manual introduction of the crack. Taking the logarithmic axis scale into account, the number of AE hits occurring before manual introduction of the crack is minor, compared to subsequent increases in hit count. After introduction of the transverse crack, fatigue testing is started on February 26, 2021. From this point in time, an increase in the AE hit count can be seen for sensor AE1, which is the AE sensor located closest to the damage and also the only AE sensor placed on the web laminate. Starting on February 27, 2021, increase in the AE hit count of sensor AE2, placed on the spar cap, is visible. This indicates that the damage has grown closer to the spar cap, where further propagation can be captured by sensor AE2. From around March 5, 2021, significant increases in AE hit counts are visible for sensors AE3 and AE4, which are also placed on the spar cap. Thus, sensors AE3 and AE4 are placed too far away from damage #3 to record the initial damage propagation. Once the damage grows to the spar cap, the AE sensors placed on the spar cap are able to detect further damage propagation. This shows that AE sensors have a limited range for detection of progressive damage growth in sandwich panels of wind turbine blades.

4.3. Distributed accelerometer system

The purpose of the distributed accelerometer system is to record data continuously and use the data for, for example, OMA of the blade. Although OMA has been chosen for the present work, other time or frequency domain methods for signal processing in the context of SHM are equally valid.

Through the use of OMA, it is not necessary to measure the excitation exerted on the structure, which substantially simplifies the measurement process. One of the basic assumptions in OMA is for the excitation to be uncorrelated white noise within the frequency range of interest. This assumption does not hold during fatigue testing, where the blade is subjected to harmonic excitation. Thus, the frequency of the harmonic excitation will be captured by the OMA algorithm, including higher-order harmonics of the excitation frequency. Similar problems would be apparent in an operating turbine, where the rotational speed of the rotor, including its higher-order harmonics, would be detected by the OMA algorithm. In such cases, it is necessary to filter out any harmonics, such that they do not pollute the results from the OMA algorithm. Considering the case of no harmonic excitation of the blade, that is, stand-still of the fatigue test, the test blade is not subjected to harmonic excitation.

Modal analysis code by Brandt (Reference Brandt, Allemang, De Clerck, Niezrecki and Wicks2013) and Orlowitz (Reference Orlowitz2015) is used to estimate the modal parameters using a correlation-function-based stochastic subspace identification (SSI) algorithm. See Brandt (Reference Brandt2011) for more details on the used OMA code and Van Overschee and De Moor (Reference Van Overschee and De Moor1996) for more details on SSI algorithms. The results from OMA are commonly visualized in a so-called stabilization diagram. Through the use of a stabilization diagram, eigenmodes of the investigated structure can be determined through visual inspection. Modes that stabilize with increasing number of poles, that is, increasing model order, have the potential for being physical modes. Computational modes, that is, nonphysical modes, will most likely also be present in a stabilization diagram, and determining the physical modes of a structure often requires prior knowledge of the structure and/or expert knowledge within the field of modal analysis.

Figure 14 shows the stabilization diagram of the blade, calculated from 10 hr of accelerometer data sampled at 25 Hz during stand-still of the fatigue test. During the course of the blade test, excitation for OMA was mainly constituted by ambient excitation inside the test hall, such as machinery, as well as footsteps inside the blade during manual inspection. A limited number of datasets with manual excitation of the blade tip is also available. Inspection of the stabilization diagram in Figure 14 shows a number of well-separated stable modes in the range between 0 and 8 Hz. At higher frequencies, modal separation is poorer, and stable physical modes are more difficult to identify. By inspecting the mode shapes corresponding to the stable poles, physical modes of the blade can be determined. By overlaying the Welch power spectral density (PSD) estimate on the stabilization diagram, physical modes can also be inferred from peaks in the PSD. The estimates of physical modes of the blade identified through OMA are listed in Table 4, together with the estimates calculated by use of the blade FE model. Eigenmodes are estimated for the blade in healthy and damaged condition, respectively, where the damage state considered is the furthest propagation of damage #3.

Figure 14. Stabilization diagram from 10 hr of accelerometer data, sampled while the fatigue test was not running. Due to the limited ambient excitation inside the test hall, long time series of data are necessary to enable estimation of lower-order modes of the blade. Crosses mark stable poles; unstable poles are not shown. Solid and dashed orange lines show the Welch PSD estimate of the flapwise and edgewise acceleration signals, respectively.

Table 4. List of eigenmodes calculated by OMA and FEA. The modes are calculated for the blade mounted with the flapwise fatigue exciter. N/A: not available; mode could not be identified consistently with OMA. Damaged state refers to the furthest propagation of damage #3, as shown in Figure 10d.

For these physical modes, no significant change in the eigenfrequencies is observed as a consequence of the furthest propagation of damage #3. This is to be expected, since the damages, which are relatively small in size compared to the span of the blade, are not assumed to significantly decrease the global stiffness of the blade, as shown by Larsen et al. (Reference Larsen, Berring, Tcherniak, Nielsen and Branner2014). Thus, the lower-order modes are not expected to be significantly impacted by the presence of the investigated damages. Damage of relatively small size compared to the length of the blade will thus only be detectable in higher-order modes, given the use of raw eigenfrequencies.

Using the blade FE model with and without the inclusion of a crack in the web, modal analyses are performed to estimate the eigenfrequencies of the blade. Deviations between the eigenfrequency estimates from OMA and FEA are attributed to measurement uncertainties, lack of excitation of individual modes, modeling inaccuracies, and uncertainties with respect to placement and weight of the fatigue exciter. The utilized FE model is not tuned for modal analysis, which is why inaccuracies in eigenfrequency estimation may be expected. The purpose of using the FE model is not to enable accurate estimation of eigenfrequencies, but instead to make possible the investigation of changes in eigenfrequencies caused by damages.

4.4. Active vibration monitoring system

Use of the presented active vibration monitoring system is investigated for blade SHM. The active vibration monitoring system, shown in Figure 6b, consists of 11 accelerometers, a force transducer, and an electrodynamical vibration shaker. Positioning of accelerometers and the vibration shaker on the web start is shown in Figure 15. The vibration signal emitted by the vibration shaker is a logarithmic chirp, with a frequency range between 100 and 3,000 Hz. The time and frequency responses of a chirp signal from both the force transducer and an accelerometer are shown in Figure 7. Using the logarithmic chirp signal as vibration input, the PSD is calculated from the sampled acceleration response. Two PSD responses for the healthy blade as well as four PSD responses with increasing severity of damage #3 are plotted in Figure 16, shown for two different accelerometers. The placement of the accelerometers relative to the vibration shaker and damage #3 is shown in Figure 14. The PSDs are calculated from acceleration time series such as the one shown in Figure 7c. Each PSD is calculated from the average of five subsequent chirp signals in order to filter out random noise. The two first PSDs are calculated based on acceleration signals from two different dates with the blade in healthy state (February 9, 2021 and February 21, 2021). Damage #3 is introduced in the web laminate on February 23, 2021. The PSDs from February 23, 2021 and subsequent PSDs are calculated from acceleration signals with increasing damage length. In general, the PSDs sampled at healthy state of the blade, at different days, show a significantly higher degree of similarity than they do with the PSDs sampled at the damaged state of the blade. With reference to Figure 16, comparing PSDs in the healthy state to PSDs in the damaged states, various differences can be observed, as explained in the following examples:

• • Peaks/valleys appearing/disappearing: Around 2,600 Hz for accelerometer #12, a peak in the PSD is present in the healthy state of the blade. With increasing damage length, the peak disappears and turns into a valley.

• • Peaks/valleys shifting frequency: Around 300 Hz for both accelerometers, valleys in the PSD decrease in frequency with increasing damage length.

• • Peaks/valleys changing magnitude: Between 1,900 and 2,100 Hz for accelerometer #9, peaks decrease in magnitude after introduction of the damage.

Figure 15. Accelerometer placement near damage #3. Accelerometers #2–7 are placed along the LE and TE between spanwise positions 8 and 14 m. This work investigates measurements from accelerometers #9 and #12.

Figure 16. Power spectral densities of chirp vibration signal, sampled at healthy and damaged states of the blade. Accelerometers #12 and #9 are placed 2 and 4 m from the damage in the spanwise direction toward the tip, respectively (see Figure 15).

The changes in the PSD for accelerometer #12 from 2 days with healthy state of the blade (February 9, 2021 and February 10, 2021), as well as the changes in PSD from the healthy to the most severely damaged state of the blade (February 9, 2021 and March 10, 2021) are highlighted in Figure 17. Changes in PSD from, for example, February 9, 2021 to February 21, 2021 are very similar to the changes observed from February 9, 2021 to February 10, 2021.

Figure 17. Change in power spectral density using accelerometer #12 between 2 days with healthy state as well as healthy and most severely damaged state. Shading with black lines highlights the change in magnitude.

Changes in the PSDs are deemed to be due to changes in the local vibration response of the blade as a consequence of initiation and progression of the damage. The change in local vibration response results in changes to the local modal parameters, which result in changes to the PSDs. As can be seen from the PSDs of the measurements from the two different accelerometers in Figure 16, the local structural response at the position of the accelerometer has a large influence on the frequencies and magnitudes of peaks in the PSDs. Thus, PSDs of the vibration measurements from accelerometers at different positions will have different magnitude distributions. As such, changes in the PSDs of vibration measurements from each individual accelerometer should be tracked over time and used as an indication for the initiation and propagation of damage.

Due to the test hall being a temperature-controlled environment, temperature effects can be ruled out as a cause for changes in the PSDs. Other possible causes for changes in the PSDs include, for example, changes in boundary conditions between the active vibration shaker and the blade. This could be due to relaxation of the adhesive used to mount the shaker, or the shaker breaking off the blade laminate. Experiments with the latter were performed, where it was found that breaking off the shaker and remounting it at a distance of 50 mm from the original position produced insignificant changes to the PSDs compared to those observed over the course of testing with damage #3. The observed changes in the PSD were comparable to the differences in the PSDs for the healthy blade shown in Figure 16, except for accelerometer #1, which was placed right next to the vibration shaker.