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Damage Detection on an Operating Wind Turbine Blade via a Single Vibration
Sensor: A Feasibility Study
Chapter · January 2021
DOI: 10.1007/978-3-030-64908-1_38
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2. Damage Detection on an Operating Wind Turbine
Blade via a Single Vibration Sensor:
A Feasibility Study
A.I. Panagiotopoulos1[0000−0002−4125−4694]
, D. Tcherniak2[0000−0001−6518−2202]
, and
S.D. Fassois1[0000−0001−6679−8690]
1
University of Patras, GR 26504 Patras, Greece
mead6601@upnet.gr (AIP), fassois@upatras.gr (SDF)
2
Bruel & Kjaer Sound & Vibration, DK 2850, Nærum, Denmark
dtcherniak@bksv.com
Abstract. Damage detection on an operating wind turbine blade is, for three pro-
gressive scenarios of 15, 30, and 45 cm long trailing edge damage, considered via a
single vibration acceleration sensor. The signals are from 3–month–long measurement
campaign, involving varying environmental and operating conditions and uncertainty
with significant effects on the dynamics, which almost completely ‘mask’ the effects of
damage. The study employs two vibration–based detection methods: A conventional
Unsupervised AutoRegressive model based (U–AR) method and a robust Unsuper-
vised Principal Component Analysis Multiple Model AutoRegressive model based
(U–PCA–MM–AR) method. The results of the study confirm the inadequacy of the
U–AR method, yet the high performance achieved by the U–PCA–MM–AR method,
which approximates well that of an 8–accelerometer–based counterpart. The study
thus suggests that high detection performance is, especially for the two ‘larger’ dam-
ages, achievable via a significantly reduced set of sensors.
Keywords: damage detection · robust methods · Structural Health Monitoring ·
uncertainty · wind turbine blade · vibration methods · statistical methods.
1 Introduction
As wind energy has been gaining importance over the past decades and wind tur-
bines have been increasing in size, their Structural Health Monitoring (SHM) has
been clearly identified as crucial for proper operation, safety, and maintenance [1,2].
In this context blades are of particular interest, with vibration–based SHM consti-
tuting an attractive technology offering no interruption of their normal operation,
“This is a pre-print of a contribution published in EWSHM 2020
(P. Rizzo and A. Milazzo, Eds.) by Springer. The final authenticated version
is available online at: https://doi.org/10.1007/978-3-030-64908-1_38

3. 2 A.I. Panagiotopoulos et al.
availability of natural excitation, and inexpensive and reliable measurement. More-
over, vibration–based techniques are global, offering the potential for low number of
sensors, automation, and continuous, real time, monitoring [1,2].
Yet, the problem is challenging, as wind turbines operate under harsh and varying
Environmental and Operating Conditions (EOCs), the effects of which on the blade
dynamics may be significant and potentially ‘masking’ those due to incipient or
early damage. Nevertheless, many of the available studies focus on damage detection
feasibility via numerical models or experiments under controlled (such as constant)
conditions in laboratory environments and, mainly, stationary (non–rotating) blades
[3,4,5,6,7].
In an effort to address the problem under realistic in–operation conditions and
uncertainty, an experimental campaign was organized on a Vestas V27 wind turbine
at the Technical University of Denmark (DTU) [8]. One of its three 1 300 cm long
blade was instrumented with a number of accelerometers, and measurements were
obtained over a 3–month–long period under the healthy state and trailing edge dam-
age of progressively increasing lengths (15, 30 and 45 cm). Additional excitation was
provided via an electromechanical actuator applying periodic impacts on the blade’s
surface.
The data from this campaign has been thus far used in a number of studies
focusing on damage detection [8,9,10] and localization [11]. The former ones attempt
detecting damage via changes in the interrelations among vibration signals measured
via 8 or 11 accelerometers mounted at various positions on the blade. The results
indicate effective detection of the two larger (30 and 45 cm) damages, and somewhat
less effective for the smallest (15 cm) one. The detection performance is degraded
when signals not employing the actuator’s impact are employed [9].
The main goal of the present study is to practically examine the feasibility of
effective damage detection under a single vibration sensor. This is an issue of ob-
vious practical importance as the use of the minimum possible number of sensors
is desired. Two Statistical Time Series type methods are employed in this context:
A conventional Unsupervised AutoRegressive model based (U–AR) method and a
robust Unsupervised Principal Component Analysis Multiple Model AutoRegressive
model based (U–PCA–MM–AR) method [12]. Signals from the experimental cam-
paign corresponding to nominal rotational speed of 32 rpm and making use of the
actuator hits are employed. Particular attention is paid to obtaining statistically re-
liable results; for this reason 7 000 inspection test cases are considered via a proper
‘rotation’ procedure with the results presented via Receiver Operating Characteristic
(ROC) curves. The main questions addressed are:
– How significant are the effects of each damage scenario on the dynamics, and
how do they compare to those of varying EOCs and uncertainty?
– Is it possible to achieve high detection performance via the single–accelerometer–
based robust U–PCA–MM–AR method?

4. Damage Detection on an Operating Wind Turbine Blade 3
Fig. 1: The Vestas V27 wind turbine in field measurements (left photo), and the 15
cm long damage on the blade’s trailing edge (right photo) [8].
– What performance is achievable by the simpler, conventional, U–AR method?
– Is the performance attained via a single vibration sensor comparable to that of
the 8–accelerometer–based method of [8]?
2 The Wind Turbine, the Damage Scenarios, and the Field
Measurements
The Vestas V27 wind turbine and the blade with an indicative damage are shown in
Figure 1. The blade is made of Glassfibre Reinforced Polyester (GRP), with its mass
being 600 kg and its length 1 300 cm. Its nominal rotational speed may be either 32
or 43 rpm, with the latter used in the present study.
The blade is instrumented with twelve lightweight accelerometers measuring the
vibration induced by normal operation and an electromechanical actuator placed near
the blade’s root (Figure 2). The actuator hits the blade every 5 minutes and 30–s–
long signals are recorded at a high sampling rate (16 384 kHz; Table 1). A typical
signal, recorded at the accelerometer employed in the present study is depicted in
Figure 3(a).
During the 3–month–long field measurements, a small defect simulating an ad-
hesive joint failure between skins (trailing edge opening) is introduced (see the right
part of Figure 1). Its original length is 15 cm (1st damage scenario), later increased to
30 cm (2nd damage scenario), and finally to 45 cm (3rd damage scenario). Thousand
of signals are recorded over the campaign period, under various Environmental and
Operating Conditions (EOCs). The present study is based on some of the measured
signals, specifically 350 corresponding to the healthy blade and 50 from each damage
scenario (see Table 2); a total of 500 signals.

5. 4 A.I. Panagiotopoulos et al.
500
6000
1300
150
300
450
7500 5500
Fig. 2: Sketch of the Vestas V27 wind turbine blade: The black circle indicates the
actuator location, the blue circle the accelerometer, and the damage location (3
scenarios) are indicated via tones of magenta.
Table 1: Vibration signal characteristics.
Sampling Signal Signal
Frequency (Hz) Bandwidth (Hz) Length (samples)
Original Signals 16 384 0 – 8192 491 520
Final Signals 4 096 850 – 1500 1 000
3 The Damage Detection Methods
The Statistical Time Series (STS) type damage detection methods employed are a
conventional Unsupervised AutoRegressive model based (U–AR), and a robust Unsu-
pervised Principal Component Analysis Multiple Model AutoRegressive model based
(U–PCA–MM–AR) method; the latter aiming at overcoming the effects of varying
Environmental and Operating Conditions (EOCs). Brief outlines of the methods are
provided below; the reader is referred to [12] for details.
Both methods include an initial baseline (learning or training) phase and an in-
spection (operating) phase. Within the context of the U–AR method, based on a single
vibration response signal obtained from the healthy structure, a proper AutoRegres-
sive (AR) model is estimated for representing the dynamics under the healthy state
(baseline phase). Once a fresh signal is available, with the structure being in unknown
state, an AR model of the same order is estimated, with the Mahalanobis distance
[13] between this and the baseline model obtained. The current structural state is
then declared as healthy / damaged as long as the Mahalanobis distance is below /
above a selected threshold (inspection phase).

6. Damage Detection on an Operating Wind Turbine Blade 5
0 200 400 600 800 1000
Time Samples
-6
-3
0
3
6
Acceleration
(m/s
2
)
Preprocessed Signal
0 1 2 3 4
Time Samples 10
5
-30
-15
0
15
30
Acceleration
(m/s
2
)
Original Signal
a b
Fig. 3: Measured random vibration signal: (a) Original signal; (b) the employed por-
tion of it following pre–processing (corresponds to the portion of the original signal
within the black frame).
Table 2: Structural states and numbers of baseline and inspection signals employed.
Structural Damage Damage Nominal Rotor Baseline Inspection
State Type Location Speed (rpm) Signals Signals
Healthy – –
[31.9 – 32.1]
300 50
15 cm Damage Adhesive joint Trailing edge 0 50
30 cm Damage failure between Trailing edge 0 50
45 cm Damage skins Trailing edge 0 50
Within the context of the U–PCA–MM–AR method a Multiple Model (MM) AR
representation (consisting of a set of p nominal AR models) is employed for best cap-
turing the healthy structural dynamics under various EOCs and uncertainty. Using
the sample covariance matrix of the AR parameter vectors, a Principal Component
Analysis (PCA) [14] is performed for AR parameter vector transformation and di-
mensionality reduction. The latter is achieved by keeping those principal components
characterized by ‘small’ variability (specifically those cumulatively explaining up to
γ % of the total variability; baseline phase). Once a fresh signal is available, with
the structure being in unknown state, an AR model of the same order is estimated
and its parameter vector is transformed by the exact same transformation procedure
used in the baseline phase. A proper pseudo–distance metric between the resulting
current vector and that of the MM representation is then computed, and the current
structural state is declared as healthy / damaged as long as the metric is below /

7. 6 A.I. Panagiotopoulos et al.
above a selected threshold (inspection phase). The pseudo–distance metric currently
employed corresponds to the minimum Jensen–Shannon divergence [15] between the
current vector and those included in the MM representation.
4 Damage Detection Performance Assessment
4.1 Vibration Signal Pre–processing
The study is based on the portion of the vibration acceleration signals enhanced by an
actuator hit. For this purpose each signal is trimmed around its periodically incurring
peak, with a small, 4 000–sample–long, portion maintained (see Figure 3(a)). This
portion of each signal is subsequently passed through a band–pass (within the 850 −
−1 500 Hz range) filter, down–sampled by a factor of 4, sample mean corrected, and
finally normalized by its sample standard deviation. The resulting vibration response
signals for damage detection are 1 000–sample–long, sampled at fs = 4 096 Hz (Table
1). A typical resulting signal is depicted in Figure 3(b).
4.2 Preliminaries
AR signal modeling is based on models of increasing order, with the proper order
selected equal to 51 via the BIC criterion [16, pp. 505–507] for the healthy blade; yet
it is maintained constant for all damage scenarios. Parameter estimation is based on
Ordinary Least Squares (Samples Per Parameter is 19.61, condition number of the
inverted matrix of order 1010
, Residual Sum of Squares over Series Sum of Squares
5.035 · 10−4
%).
The effects of each damage scenario on the dynamics, as reflected in the AR(51)
model based Power Spectral Density (PSD) of the (pre–processed) vibration signals,
are presented in Figure 4. In this, the PSDs of a set of 300 healthy baseline sig-
nals are contrasted to those of 50 healthy inspection signals, as well as to those of
50 inspection signals from each damage scenario (also see Table 2). Based on the
obtained plots, very significant variability in the healthy PSD (and hence the un-
derlying dynamics) is evident due to the varying EOCs (Figure 4(a)). Moreover, the
effects of the 15 cm damage scenario on the PSD are relatively minor compared to
the healthy PSD variability, as the zone of the former PSDs largely lies within the
zone of their healthy counterparts (Figure 4(b)). The situation is only slightly better
(somewhat increased discrepancies between the two classes) for the 30 cm damage
scenario (Figure 4(c)), and further improved (further pronounced discrepancies) for
the 45 cm damage scenario (Figure 4(d)). These observations underline the difficult
nature of the damage detection problem.

8. Damage Detection on an Operating Wind Turbine Blade 7
Fig. 4: The effects of each damage scenario on the pre–processed vibration signal
AR(51) model based Power Spectral Density (PSD). Healthy (300 baseline signals)
versus 50 inspection signal PSDs from the: (a) Healthy state, (b) 15 cm damage state,
(c) 30 cm damage state, (d) 45 cm damage state. (Overlaps render color mixing.)
Table 3: Damage detection method and performance assessment details.
Method Baseline Threshold # Thresholds # Inspection # Inspection
Signals γ (%) employed Signals Test Cases
(ROC curve) (per Health State (per Health State
and ‘rotation’) for all ‘rotations’)
U–AR 1 –
104
50 1750
U–PCA–MM–AR 300 99.5
4.3 The Damage Performance Assessment Procedure
In order to achieve proper and statistically reliable performance assessment, a ‘rota-
tion’ procedure is adopted. In each ‘rotation’ 300 healthy signals are included in the
baseline phase, while 50 signals per health state (healthy, 15, 30, and 45 cm damage)
are reserved for testing in the inspection phase (see Table 2). In each ‘rotation’ the
50 healthy inspection signals are interchanged with counterparts previously included
in the baseline phase. This warrants a proper overall assessment that is not sensitive
to the particular healthy signals selected for the baseline and the inspection phases,
and also leads to a high number of test cases (1 750 per health state and 7 000 in
total for 35 ‘rotations’; see Table 3).

9. 8 A.I. Panagiotopoulos et al.
0 1750 3500 5250 7000
Inspection Experiment
10
20
30
40
50
d
U-AR
0 1750 3500 5250 7000
Inspection Experiment
10
15
20
25
30
D
PCA
(m
o
,m
u
)
U-PCA-MM-AR
Healthy 15 cm Damage 30 cm Damage 45 cm Damage
a b
Fig. 5: The pseudo–distance metric for each inspection experiment (test case): (a)
U–AR method, (b) U–PCA–MM–AR method. (All 7 000 inspection experiments.)
4.4 Assessment Results
Damage assessment results are, for each method and each health state, provided in
terms of pseudo–distance metrics in Figure 5 and in terms of Receiver Operating
Characteristic (ROC) Curves [17] in Figure 6. In the latter (ROC) case the curve
corresponding to the 8–accelerometer–based method of [8] is also provided for com-
parison.
Based on the pseudo–distance metric plots, it is evident that the robust U–PCA–
MM–AR method performs much better than its simpler U–AR counterpart; a fact
clearly expected. Moreover, there is an upward, monotonic, trend in the plots of both
methods as the damage level (length) increases, which is logical and suggests that
the pseudo–distance metrics are reliable indicators of the damage level.
The ROC curves confirm the above observations, reflecting inadequate perfor-
mance (for the 15 and 30 cm damages) of the simple U–AR method, but very good
performance of the robust U–PCA–MM–AR method. More specifically, the perfor-
mance of the latter method is adequate (64% TPR for 5% FPR) for the 15 cm damage
scenario, very good (100% TPR for 2.7% FPR) for the 30 cm damage scenario, and
excellent (ideal) for the 45 cm damage scenario (TPR: True Positive Rate or correct
detection rate; FPR: False Positive Rate or false alarm rate). It is worthwhile to ob-
serve that the 8–accelerometer–based method in [8] achieves improved performance
only for the ‘smallest’ 15 cm damage case, while identical performance to that of the
single–accelerometer–based U–PCA–MM-AR method is essentially attained for the
other two damage scenarios.

10. Damage Detection on an Operating Wind Turbine Blade 9
Fig. 6: Damage detection performance assessment via ROC curves for the U–AR (red
curve), U–PCA–MM–AR (green curve), and the 8–accelerometer–based method of
[8] (orange curve): (a) 15 cm damage, (b) 30 cm damage, and (c) 45 cm damage.
5 Concluding Remarks
The problem of damage detection on an operating wind turbine 1 300 cm long blade
has been considered via a single vibration acceleration sensor. The main conclusions
from the study may be summarized as follows:
– The effects of damage on the dynamics are relatively minor and to a considerable
extent ‘masked’ by those of varying EOCs and uncertainty.
– The single–accelerometer–based robust U–PCA–MM–AR method has been shown
to achieve adequate performance for the 15 cm damage, very good for the 30 cm
damage, and excellent for the 45 cm damage.
– As expected, the conventional U–AR method’s performance seriously lags behind,
being generally inadequate.
– The detection performance attained via a single vibration sensor and the robust
U–PCA–MM–AR method is comparable (primarily for the two ‘larger’ damage
scenarios) to that of the 8–accelerometer–based method in [8], thus suggesting
that a significantly reduced set of vibration sensors is potentially adequate.
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