Renewable Energy 46 (2012) 169e178
Contents lists available at SciVerse ScienceDirect
Renewable Energy
journal homepage: www.elsevier.com/locate/renene
Condition monitoring of wind turbines: Techniques and methods
Fausto Pedro García Márquez a, *, Andrew Mark Tobias b, Jesús María Pinar Pérez a, Mayorkinos Papaelias b
a
b
Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain
University of Birmingham, Birmingham B15 2TT, United Kingdom
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 15 September 2011
Accepted 3 March 2012
Available online 5 April 2012
Wind Turbines (WT) are one of the fastest growing sources of power production in the world today and
there is a constant need to reduce the costs of operating and maintaining them. Condition monitoring
(CM) is a tool commonly employed for the early detection of faults/failures so as to minimise downtime
and maximize productivity. This paper provides a review of the state-of-the-art in the CM of wind
turbines, describing the different maintenance strategies, CM techniques and methods, and highlighting
in a table the various combinations of these that have been reported in the literature. Future research
opportunities in fault diagnostics are identified using a qualitative fault tree analysis.
Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved.
Keywords:
Fault detection and diagnosis
Condition monitoring
Maintenance management
Wind turbines
1. Introduction
Only time will tell whether the forecasts by WWEA [1] for
continuing growth in global wind power capacity [2] will be realised as shown in Fig. 1. But to make wind power competitive with
other sources of energy, availability, reliability and the life of
turbines will all need to be improved. As the wind energy sector
grows, business economics will demand increasingly careful
management of costs. For a 20-year life, the operations and maintenance (O&M) costs of 750 kW turbines might account for about
25%e30% of the overall energy generation cost [3] or 75%e90% of
the investment costs [4].
Furthermore, one projection in 2002 was that the O&M costs for
2 MW turbines (which together with 2.5 and 3 MW turbines have
since become the workhorses of the wind power industry) “might
be 12% less than an equivalent project of 750 kW machines” [5]. But
new wind farms typically have higher capacity and comprise more
machines. The turbine data in Fig. 2 [6] suggest that larger turbines
fail more frequently and thus require more maintenance. Reducing
inspection and maintenance costs has thus become increasingly
important as wind turbine size and numbers have continued to rise.
Of course, some WT components fail earlier than expected
and, because unscheduled downtime can be so costly [7], condition
monitoring systems (CMS) are employed to “improve WT
* Corresponding author.
E-mail addresses: Faustopedro.garcia@uclm.es (F.P. García Márquez),
a.m.tobias@bham.ac.uk (A.M. Tobias), jesusmaria.pinar@uclm.es (J.M. Pinar Pérez),
m.papaelias@bham.ac.uk (M. Papaelias).
availability and reduce the O&M costs” [8]. However there is
a degree of uncertainty about the appropriateness of applying
specific maintenance policies to WT components. This paper
discusses the applicability of various maintenance strategies to WT
condition monitoring, reviews the available techniques and
methods in the literature, and presents a fault tree analysis (FTA)
summarising the ways in which WTs can fail. The discussion
focuses on the three blade up-wind variable speed turbine with
double feed asynchronous generator which is the dominant type of
WT currently in use worldwide [9].
2. Wind turbines
Most WT machines are three-blade units comprising the major
components illustrated in Fig. 3 [10]. Driven by the wind, the blades
and rotor transmit energy via the main shaft through the gearbox to
the generator, the main shaft being supported by the bearings, and
the gearbox being such that the generator speed is as near as
possible to optimal for the generation of electricity. Alignment with
the direction of the wind is controlled by a yaw system and the
housing (or “nacelle”) is mounted at the top of a tower.
Some defects such as leaking and corrosion can be detected by
visual inspection; discolouration of component surfaces may indicate slight temperature variations or deteriorating condition, and
the sound coming from the bearings can also indicate physical
condition [11,12]. However, many of the most typical failures like
cracking and roughness on the surfaces of the blades, electric short
circuits in the generator, and overheating of the gearbox all demand
a more sophisticated approach to maintenance.
0960-1481/$ e see front matter Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.renene.2012.03.003
170
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
Fig. 1. Wind Energy: Global Capacity (green) and Forecast (red) [1]. (For interpretation
of the references to colour in this figure legend, the reader is referred to the web
version of this article.)
3. Maintenance theory
Maintenance is required to make sure that the components
continue to perform the functions for which they were designed.
The basic objectives of the maintenance activity are to deploy the
minimum resources required to ensure that components perform
their intended functions properly, to ensure system reliability and
to recover from breakdowns [13].
3.1. Corrective, scheduled and condition based maintenance
Classical theory sees maintenance as either corrective or
preventive. The former (also known as unscheduled or failure
based maintenance) is carried out when turbines break down and
when faults are detected or failures occur in any of the components.
Immediate refurbishment or replacement of parts may be necessary [14] and unscheduled downtime will result. Corrective maintenance is therefore the most expensive of strategies and wind farm
operators will hope to resort to it as little as possible. The various
stages are shown in Fig. 4.
By contrast, the objective behind preventive maintenance (PM)
is to either repair or replace components before they fail as shown
in Fig. 5 [14]. This has most straightforwardly been achieved by
scheduled maintenance, also known as time based (or planned)
Fig. 3. Main parts of a turbine (Source: [11]) showing (1) blades, (2) rotor, (3) gearbox,
(4) generator, (5) bearings, (6) yaw system and (7) tower.
maintenance and involving repair or replacement at regular time
intervals as recommended by the supplier and regardless of
condition. Scheduled maintenance activities in WT include the
changing of oil and filters, and the tightening and torqueing of
bolts.
But reducing failures in this way comes at the cost of completing
maintenance tasks more frequently than absolutely necessary and
not exhausting the full life of the various components already inservice. An alternative is to mitigate against major component
failure and system breakdown with condition based maintenance
(CBM) in which continuous monitoring and inspection techniques
are employed to detect incipient faults early, and to determine any
necessary maintenance tasks ahead of failure [15]. This involves
acquisition, processing, analysis and interpretation of data and
selection of optimal maintenance actions [16] and is achieved using
condition monitoring systems [17e19]. CBM has been shown to
minimise the costs of maintenance, improve operational safety, and
reduce the quantity and severity of in-service system failures. Byon
and Ding [20] have demonstrated its applicability to WTs, and
McMillan and Ault [21] have used Monte Carlo simulation to
evaluate its cost effectiveness when applied to WTs. CBM is now the
most widely employed strategy in the WT industry.
3.2. Reliability centred maintenance
The state-of-the-art way of deciding upon maintenance strategy
in the WT industry is reliability centred maintenance (RCM), which
has been formally defined as “a process used to determine what must
be done to ensure that any physical asset continues to do whatever its
users want it to do in its present operating context” [22]. It involves
maintaining system functions, identifying failure modes, prioritizing functions, identifying PM requirements and selecting the
most appropriate maintenance tasks [23] with the objective of
managing system failure risk e((ffectively [24]; operational and
maintenance policies are optimised such that the overall maintenance task is reduced [14]. RCM has been recognized and accepted
in many industrial fields, such as steel plants [25], railway
networks [26] and [27], ship maintenance and other industries
[28]. RCM in the wind turbine industry is addressed by Andrawus
et al. [29].
Fig. 2. Distribution of failure frequencies between different turbine types, sorted by
turbine size [6].
Fig. 4. Stages of a corrective maintenance task.
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
Fig. 5. Stages of a preventive maintenance task.
4. Condition monitoring of wind turbines
On the basis that a “significant change is indicative of a developing failure” [30], condition monitoring systems (CMS) [13]
comprise combinations of sensors and signal processing equipment that provide continuous indications of component (and hence
wind turbine) condition based on techniques including vibration
analysis, acoustics, oil analysis, strain measurement and thermography. On WTs they are used to monitor the status of critical
operating major components such as the blades, gearbox, generator, main bearings and tower. Monitoring may be on-line (and
hence provide instantaneous feedback of condition) or off-line
(data being collected at regular time intervals using measurement
systems that are not integrated with the equipment) [31].
With good data acquisition and appropriate signal processing,
faults can thus be detected while components are operational and
appropriate actions can be planned in time to prevent damage or
failure of components. Maintenance tasks can be planned and
scheduled more efficiently, resulting in increased reliability, availability, maintainability and safety (RAMS) whilst downtime,
maintenance and operational costs are reduced [8]. CM techniques
are thus used throughout the industry [32,33] and benefits are
“especially shown for offshore wind farm(s)” [34] because of not
only the high costs of O&M at sea but also the typically larger
turbines. Several techniques are available.
4.1. Vibration analysis
Vibration analysis continues to be “the most popular technology employed in WT, especially for rotating equipment” [35].
Different sensors are required for different frequencies: “position
transducers are used for the low-frequency range, velocity sensors
in the middle frequency area, accelerometers in the high frequency
range and spectral emitted energy sensors for very high frequencies” [36].
As for applications, it is appropriate for monitoring the gearbox
[37e43], the bearings [11,12,44e46] and other selected WT
elements as listed in Table 1; the sensor configuration in a nacelle is
illustrated in [37]. Furthermore “Tandon and Nakra [47] presented
a detailed review of the different vibration and acoustic methods,
such as vibration measurements in the time and frequency
domains, sound measurements, the shock pulse method and the
acoustic emission technique, for CM of rolling bearings” [48].
4.2. Acoustic emission
Rapid release of strain energy takes place and elastic waves are
generated when the structure of a metal is altered, and this can be
analysed by acoustic emissions (AE). The primary sources of AE in
WTs are the generation and propagation of cracks, and the technique has been found [49e51] to detect some faults earlier than
others such as vibration analysis [52]. The measurement and
interpretation of AE parameters for fault detection in radially
loaded ball bearings has been demonstrated at different speed
ranges in [53]. In addition, the application of AE for the detection of
bearing failures has been presented in [54]. Acoustic monitoring
has some similarities with vibration monitoring but whereas
“vibration sensors are mounted on the component involved” [36]
so as to detect movement, acoustic sensors are attached with
171
flexible glue with low attenuation and record sound directly. AE
sensors have been used successfully not only in the monitoring of
bearings and gearboxes but also for damage detection in blades of
a WT as discussed in [55]. Its application is also possible to an inservice WT for a real-time rotating blade [56,57]. Non-destructive
testing techniques using acoustic waves to improve the safety of
wind turbine blades are presented in [58], and to enable the
assessment of the damage criticality for blades of small WT based
on AE data in [59,60]. The use of AE is gradually growing for both
CM of rotating WT components as well as blades.
4.3. Ultrasonic testing techniques
Ultrasonic testing (UT) techniques are used extensively by the
wind energy industry for the structural evaluation of wind turbine
towers and blades. UT is generally employed for the detection and
qualitative assessment of surface and subsurface structural defects
[13,25,61]. Ultrasonic wave propagation characteristics allow estimation of the location and type of defect detected, thus providing
a reliable method of determining the material properties of the
principal turbine components. Signal-processing algorithms
including time-frequency techniques and wavelet transforms can
be used to extract more information [62e65]. An ultrasound
technique to visualize the inner structure of wind turbine blades is
presented in [66], the deployment of UT techniques for inspection
of the whole multi-layered structure of the WT blade and to find
defects like delaminations, lack of glue, etc is illustrated in [67]; and
the ultrasonic air-coupled technique has been used to research
internal defects in wind turbine blades [68]. Ultrasonicallyobtained images make it possible to recognise the geometry of
defects and to estimate their approximate dimensions. Jasiuniene
et al. [69] adapted air-coupled ultrasonic technique for better
identification of the shape and size of defects in a WT blade. A
review of other methods can be found in [70].
4.4. Oil analysis
Whether for the ultimate purpose of guaranteeing oil quality or
the condition of the various moving parts, “oil analysis is mostly
executed off-line by taking samples” [35] despite on-line sensors
having (for years) been “available at an acceptable price level”
[35,36] for monitoring oil temperature, contamination and moisture [71]. Little or no vibration may be evident while faults are
developing, but analysis of the oil can provide early warnings;
a case study of a WT gearbox is described in [72]. In the “case of
excessive filter pollution, oil contamination or change in component properties, characterization of the particulates can give an
indication of excessive wear” [36]. Such approaches are particularly
effective and cost-efficient in avoiding catastrophic failures [73,74].
On-line oil analysis is gradually becoming more important with
several on-going pilot projects.
4.5. Strain measurement
Strain measurement using strain gauges can be very useful for
lifetime forecasting and protecting against high stress levels,
especially in the blades. An assessment of strain gauge signal
interpretation from strain gauge sensors installed on the blade
has been performed in order to adjust calibration practices and
sensor selection [75]. Optical fibre sensors are still very expensive
[35] but cost-effective systems based on fibre optics are being
developed. References [56,76,77] illustrate how load monitoring
can be performed using strain sensors in the rotor blades. Strain
measurement can be expected to grow in importance as an input
to CM.
172
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
Table 1
Condition monitoring techniques in WT, where. a: Statistical methods; b: Time domain analysis; c: Cepstrum analysis; d: Fast Fourier transformation(FFT); e: Amplitude
demodulation; f: Wavelet transformation; g: Hidden Markov models; h: Novel techniques.
Blades
Vibration
Acoustic emission
Ultrasonic techniques
Oil analysis
Strain
Electrical effects
Shock Pulse methods
Process parameters
Performance monitoring
Radiographic inspections
Thermography
Other
Rotor
Gearbox
Generator
[56], a [134], f[135]
c[37], c[108], a[113] d[10], d[32], a[37], c[38], b[39],
f[40], b[41], e[42] bef[43], eb[104],
c[107], f[119], f[125], fg[126,136],
c[106], c[137]
[56], a[59,88,140], a[141], f[143]
a[95], f[125]
a[55,57,58,60,142]
f[63,140], a[67e70]
b[72], a[95]
[146]
[56,75,88,147], c[77]
[148]
[79,81]
a[94]
Bearings
Tower
d[10], a[37], c[38], a[44], d[11],
d[10]
d[12] a[45], df[46], c[107], f[120],
f[123], g[130], f[135], f[138], f[139]
a[53], de[84], b[105,144], a[145]
a[74]
[80]
de[84], a[82], f[19,83]
a[87]
[88]
[69,91]
[93e95]
[95,150,151]
c[149]
a[86]
a[86], b[89] cd[149]
h[87]
h[87]
4.6. Electrical effects
CM of electrical equipment such as motors, generators and
accumulators is typically performed using voltage and current
analysis. Discharge measurements are used for medium and high
voltage grids. A spectral analysis of the stator current [78] in the
generator can be used for detecting isolation faults in the cabling
without influencing WT operation.
Electrical resistance can also be used for the structural evaluation
of certain WT components. Electrical resistance varies with stiffness
and abrupt changes can be used to detect cracks, delaminations, and
fatigue. Hence, the technique can be applied to in-service WTs.
References [79e81] demonstrate how the resistance principle is
useful for detecting fatigue damage in particular. These techniques
are at the moment confined to research-related activities but there is
significant potential for applying them successfully in the field.
4.7. Shock pulse method (SPM)
The SPM has been used as a quantitative method for condition
monitoring of bearings, and works by detecting the mechanical
shocks that are generated “when a ball or roller in a bearing comes
in contact with a damaged area of raceway or with debris” [82].
Signals are picked up by transducers, and analysis - e.g. using
a normalized shock value [83]- gives an indication of system
condition [84]. Low frequency signals of vibration collected in the
nacelle and caused by other sources can easily be filtered electronically. A case study of SPM with a piezoelectric transducer is
described in [85]. The method is occasionally used by the industry
to support vibration measurements.
4.8. Process parameters
Maintenance based on process parameters and the detection of
signals exceeding predefined control limits is common practice in
WTs, control systems becoming increasingly sophisticated and
diagnostic capabilities ever better. Transient and oscillatory stability
were analysed with different wind scenarios for electricity generation process in [86]. For explanation of the use of signals and
trending for fault detection based on parameter estimation, see [87].
4.9. Performance monitoring
The relationship between parameters such as power, wind
speed, blade angle and rotor speed can also be used for an
[11,12,151]
assessment of WT condition and for the early detection of faults
[88]. Previous work includes power and voltage flicker analysis
with variable wind speed and turbulence [86]. “Similar to estimation of process parameter(s), more sophisticated methods,
including trending, are not often used” [36]. The torque and power
generated with wind time series taken in the field from an
anemometer is considered in [89].
4.10. Radiographic inspection
Radiographic imaging of critical structural turbine components
using X-rays is only rarely used although it does provide useful
information regarding the structural condition of the component
being inspected. Radiographic imaging depends “on the different
level of absorption of X-ray photons as they pass through
a material” [70]. So as “to detect tight delaminations or cracks,
having gaps less than 50 mm, the backscatter X-ray imaging
technique” [70] is employed. X-Ray imaging is useful to locate the
internal defects of the WT, and the main advantage of X-ray
inspection is the accuracy of this technique [90]. A transportable
radiographic system for WT blades has been recently demonstrated as “a solution to find defects and reduce the cost of
inspection” [91].
4.11. Thermography
Thermography is often used for monitoring electronic and
electric components and identifying failure [92]. The technique is
only applied off-line, and often involves visual interpretation of
hot spots that arise due to bad contact or a system fault. At
present the technique is not particularly well-established for online CM, but cameras and diagnostic software that are suitable
for on-line process monitoring are starting to become available.
Infrared cameras have been used to visualize variations in blade
surface temperature [93e95] and can “effectively indicate cracks
as well as places threatened by damage” [96]. In the longer term,
this might be applicable to WT generators and power electronics
too.
Pulsed thermography can be employed for the structural evaluation of blades but due to the bulky equipment involved this is not
a standard methodology amongst wind turbine operators. Early
investigations of carrying out thermographic measurements of inservice blades using helicopters to deploy the IR cameras has not
yet been proven satisfactorily and faces serious difficulties of
implementation.
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
5. Sensory signals and signal processing methods
Regardless of the technique, the capability of a CMS relies upon
two basic elements: the number and type of sensors, and the
associated signal processing and simplification methods utilized to
extract important information from the various signals. An electronic measurement system will acquire the data, and then process
and distribute them to an observer or other technical control
system.
Data acquisition will involve measuring the required variables
(e.g. current, voltage, temperature, speed) and turning them into
electronic signals. To do so effectively will involve judicious choice
and placement of the right type and number of sensors; conditioning (performing basic operations including amplification,
filtering, linearization, and finally modulation/demodulation) may
be necessary to reduce the susceptibility of the signals to interference. Optimization techniques may then be employed [97] in
the processing of the signals by a digital signal processor (DSP),
involving not only the processing itself but also sorting and
manipulation as necessary. Subsequent distribution will be to
either a screen, computer, storage device or other system. There
are several options, including e.g. Ethernet networks with TCP/IP
protocol together with WLAN for a WT for communicating with
either a Farm Server or a supervisory command and data acquisition (SCADA) system. The latter is a particular computer-based
system that allows local and remote control of the functions
of a WT, gathering data from the wind farm and analysing them
in order to report operational performance, and hence ensure
efficient operation. SCADA uses various signal processing
methods, the most relevant to WT being those covered below and
in Table 1.
5.1. Statistical methods
One common application of statistical algorithms for the
purposes of CM is to analyse the data signals from the various
sensors in WTs. Common statistical measures such as root mean
square (RMS) and peak amplitude are widely used for the diagnosis
of failures but more advanced features are also being developed
[37,74,82,86,98,99]. Other important statistical parameters are the
maximum value, minimum value, mean, peak to peak, standard
deviation, shape factor, crest factor, impulse factor, definite integral,
energy ratio and kurtosis.
5.2. Trend analysis
When applied to WT, trend analysis refers to the concept of
collecting data from the various sensors and looking for trends. It
requires particular algorithms [37,100,101], and applications
include the monitoring of pitch mechanisms although most
common is its use on power output patterns from WT generators.
Note that trend estimation is a different technique specifically for
forecasting.
5.3. Filtering methods
Sometimes there are redundant data that contain information
that is not useful and must be eliminated so as to avoid compromising the computations. For example, the vibrations from
a nacelle will have to be filtered whilst measuring the vibration of
the gearbox inside [84]. A method using least median squares that
filters in parallel with estimating the power curve is presented in
[102], and filtering with and without a classical statistical method
(based on standard deviation) is described and compared in [103].
The drawback of filtering whilst monitoring trends is that the
173
parameters have to be adjusted to take account of varying operating
conditions.
5.4. Time-domain analysis
Analysis in the time domain is a way of monitoring WT faults like
resistive and inductive imbalances between the rotor and the stator
phases, and turn-to-turn faults in the rotor windings of the generator. Variations in current signals and trends are typically used for
vibration analysis [39,41,104], oil analysis [72] and AE [105].
5.5. Cepstrum analysis
The power cepstrum is a time based approach defined as “the
inverse Fourier transform of the logarithmic power spectrum”
[106]. The cepstrum is well suited for applications to equipment
diagnostics in a WT. Gear vibration spectra commonly show sidebands of meshing frequency and its harmonics arising from the
modulation of tooth meshing waveform [38,107]. For gearboxes in
good condition, the sideband level generally remains constant with
time. Therefore, changes in the number and amplitude of the
sidebands normally indicate deterioration. The presence of several
families of sidebands and other components can complicate the
distinction and evaluation of the sideband spacing [108].
5.6. Time synchronous averaging (TSA)
TSA, also called time domain averaging, is a signal processing
technique that serves as the basis for many gear fault detection
algorithms. The method is for identifying a rotating bearing defect
by measuring the vibration of a rotating bearing and obtaining
a waveform signal. It may also help in identifying the source of
vibration in WT gearboxes [109]. For instance, a cracked gear tooth
in a WT gearbox that meshes once per revolution produces a highly
periodic vibration signature which can be very weak. TSA can be
used to highlight the vibration signal features taking place over
a given period, and can work with non-periodic signal components,
filtering the noise of the time-domain signal. Once the average time
signal is achieved, it is possible to compute the FFT (as below). A
review of other TSA algorithms is presented in [110].
5.7. Fast-Fourier transform (FFT)
The FFT [111] algorithm is used for the conversion of a digital
signal from the time domain into one in the frequency domain.
Particular frequency ranges correspond to particular states
(e.g. fault-free, defective bearing), the ranges reflecting the rotational speed of the main shaft and the shape and size of the element
concerned. All bearing elements generate vibration at specific
frequencies (referred to as fault frequencies) and hence FFT finds
most usage in gearbox monitoring [10,32]. FFT is also used for
bearings [10,11] wherein, if damage starts to develop, the shape of
the vibration distribution deviates from the nominal Gauss-shaped
curve [11,12]. “The advantage of frequency domain analysis over
time domain analysis is its ability to identify and isolate certain
frequency components of interest” [112].
5.8. Amplitude demodulation
This approach can extract very low-amplitude and low-frequency
periodic signals that might be masked by other higher energy
vibrations as in WT gearboxes [43,104]. While the raw spectrum can
be useful for monitoring gear mesh frequencies, the envelope spectrum provides superior sensitivity to bearing defect frequencies in
WT applications [84]. With its high sensitivity, demodulation has
174
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
been proven to be good for evaluating defects that produce impacting, e.g. rolling contacts in bearings and tooth-to-tooth contacts in
the gear meshes [42]. It also helps to reduce the complexity of the
analysis, the main advantage being that it provides excellent visibility
of bearing defects frequencies without the interference of gear mesh
frequencies in the same spectrum. Despite the advantages for WT
gearboxes, some barriers to implementation remain. The selection of
transducer location is crucial to guarantee the results and, if the
demodulation process is based on low-pass filtering, the original
amplitude of the original defect signals is not preserved.
5.9. Order analysis
FFT is useful for studying oscillations in constant speed wind
energy converters (WEC) when applied to time series signals.
However, this algorithm is not suitable for variable speed WEC
where a different algorithm based on rotational angles is required,
i.e. so-called order analysis, particularly well-suited for rotor
imbalances and aerodynamic asymmetries [38]. Torsional oscillation of the nacelle generates a signal which is out of phase with any
transverse oscillations, so they can be separated and analysed
individually [113]. Interpolation and production of the order spectrum then leads to the computation of the FFT. Such analysis may be
used for monitoring the overall rotor condition including surface
roughness, mass imbalance and aerodynamic asymmetry.
5.10. Wavelet transforms
Wavelet transformation is a time-frequency technique similar
to short time Fourier transform (STFT) but more appropriate for
non-stationary signals. It provides a time-frequency 3D map of
the signal being analysed in [114,115] and involves decomposing
it into a set of sub-signals or levels with different frequencies
[116,117]. It is applied to WTs in order to monitor the vibration
level caused by misalignment, bearing and other problems, and
can be used as a general sign or indication of a faulty WT.
Wavelet transforms have been applied to waveform data analysis in fault detection and diagnostics of various WT parts
including gears [118,119], bearings [120,121], and other
mechanical systems [122,123]. Assessment of the effectiveness
and reliability of wavelet transforms and comparison with other
vibration analysis techniques has been completed by Dalpiaz
and Rivola [124]. Baydar and Ball [125] successfully applied
wavelet transforms to vibration signals and acoustic signals
[40,43,126]. FFT (and indeed RMS) have also been used to estimate the maximum amplitude of the wavelet coefficients which
helps in discriminating between normal and damaged components [127].
5.11. Hidden Markov models
Hidden Markov models (HMM) have successfully been applied
to the classification of patterns in trend analysis [128] and CM
[126]. Atlas et al. developed a method to predict wear accurately
[129], when applying a method for monitoring of milling processes
with HMM. “Ocak and Loparo [130] presented the application of
HMM in bearing fault detection” [126], and the dynamic statistical
characteristics that exist in the current observations of vibration
signals in the machine have been modelled using HMM utilizing
a Markov chain.
Fig. 6. Fault tree for WT.
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
5.12. Novel techniques
Fault detection and diagnosis (FDD) is a sophisticated adaptation of CMS that incorporates ‘intelligent’ algorithms suitable for
early detection of incipient faults providing an insight into the
corresponding level of criticality [19]. FDD methods can be modelbased or non-model based depending upon the way process
knowledge is incorporated within the signal processing unit;
a more specific classification is shown in [38].
Artificial intelligence (AI) is essentially employed to reproduce
human reasoning as accurately as possible, the reasoning process
being based on the behaviour of the system, and written down in
terms of rules. The dynamic nature of the environments in which
WTs operate has led to the emergence of predictive maintenance
plans that take qualitative account of the environment as well as its
actual effect on the condition of the components.
Expert systems or so-called rule-based diagnostic systems
“detect and identify incipient faults in accordance with the rules
representing the relation of each possible fault with the actual
monitored equipment condition” [131]. They have to fulfil two
capabilities so as to be effective: the acquisition and integration of
new knowledge, and also the explanation of their reasoning [132].
6. Fault tree analysis (FTA)
For detailed CM study, identification of potentially hazardous
events and an assessment of their consequences and frequency of
occurrence are necessary. One of the most popular approaches for
this purpose is FTA [133], a so-called “fault tree” (FT) being a diagrammatic way of describing the complete set of possible causes
that can lead to failure. FTA thus allows identifying and then
quantifying the initiating failure causes that will help set the stage
for developing a PM program fit to maintain system reliability at
the required level with particular attention given to aggressive
environmental factors. The process involves two stages: selection of
the various components that are to be considered for analysis, and
then assessment of how the component states affect the condition
variables that will be measured by the CM system. Both are critical
and have a significant impact on the accuracy of the model.
Here, the FT reproduced in Fig. 6 was constructed starting with
the top event: wind turbine failure. For each of the same components as highlighted in Fig. 3 in turn, this was then successively
broken down via the various possible intermediate failures using
AND and OR logic gates so as finally to yield the basic failure events
at the lowest levels e.g. corrosion of pins, pitting, deformation of
the outer bearing race and other rolling elements, or indeed abrasive wear [35].
7. Conclusions
The primary focus of this review of CM and the various mathematical methods for signal processing is upon WT gearboxes and
bearings, rotors and blades, generators and power electronics,
rather than system-wide turbine diagnosis. An inventory of the
available CM techniques along with signal processing algorithms
has been provided and selection of a set of techniques which is
feasible and better suited for WTs has been made possible. The
review is summarised in Table 1, which may be read from the
viewpoint of either techniques (row-wise) or components
(column-wise). The references of the different methods used by the
researchers in the components of WT and techniques for CM are
collected in the next table. The methods reported in the literature
are indicated by the letters.
For each element of the WT there are different techniques that
can be employed, and for all of these techniques there are
175
mathematical methods available and referenced in the literature.
The main obstacles facing the designers of condition monitoring
systems for wind turbines clearly continue to be:
i. selection of the number and type of sensors;
ii. selection of effective signal processing methods associated
with the selected sensors; and
iii. design of an effective fusion model (i.e., the combination of
sensors and signal processing methods which give an
improved performance);
Acknowledgement
This work has been supported by the European Project
“Development and Demonstration of a Novel Integrated Condition
Monitoring System for Wind Turbines” (NIMO, Ref.:FP7-ENERGY2008-TREN-1: 239462, www.nimoproject.eu).
Acronyms
AE
AI
CBM
CM
CMS
FDD
FFT
FT
FTA
HMM
O&M
PM
RMS
RCM
SCADA
SPM
TSA
UT
WEC
WT
acoustic emission
artificial intelligence
condition based maintenance
condition monitoring
condition monitoring system
fault detection and diagnosis
fast Fourier transform
fault tree
fault tree analysis
hidden Markov model
operation and maintenance
preventive maintenance
root mean square
reliability centred maintenance
supervisory command and data acquisition
shock pulse method
time synchronous averaging
ultrasonic testing
wind energy converter
wind turbine
References
[1] WWEA. World wind energy report 2009, http://www.wwindea.org; 2009.
[2] McGowan JG, Connors SR. Windpower: a turn of the century review. Annual
Review of Energy and the Environment 2000;25:147e97.
[3] Milborrow D. Operation and maintenance costs compared and revealed.
Wind Stats 2006;19(3):3.
[4] Vachon W. Long-term O&M costs of wind turbines based on failure rates and
repair costs. In: Proceedings WINDPOWER, American wind energy Association Annual conference. Portland: Oregon; 2002. p. 2e5. June.
[5] Walford C. Wind turbine reliability: understanding and minimizing wind
turbine operation and maintenance costs. Sandia report, SAND2006-1100.
Albuquerque, New Mexico 87185 and Livermore, California 94550: Sandia
National Laboratories; 2006.
[6] Tavner PJ, Spinato F, van Bussel GJW, Koutoulakos E. Reliability of different
wind turbine concepts with relevance to offshore application. Brussels:
European Wind Energy Conference; April 2008.
[7] Anon. Managing the wind: reducing Kilowatt-Hour costs with condition
monitoring. Refocus 2005;6(3):48e51.
[8] Yang WX, Tavner PJ, Crabtree CJ, Wilkinson M. Cost effective condition
monitoring for wind turbines. IEE Transactions on Industrial Electronics
2010;57(1).
[9] Wind Power in the United States: Technology, Economic, and Policy Issues
http://www.fas.org/sgp/crs/misc/RL34546.pdf (02-09-09).
[10] de Novaes Pires G, Alencar E, Kraj A. Remote conditioning monitoring system
for a hybrid wind diesel system-application at Fernando de Naronha Island,
Brasil. http://www.ontario-sea.org (19-07-10).
176
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
[11] Igarashi T, Hamada H. Studies on the vibration and sound of defective roller
bearings (First report: vibration of ball bearing with one defect), Bull. JSME
June 1982;25(204):994e1001.
[12] Igarashi T, Yabe S. Studies on the vibration and sound of defective roller
bearings (First report: sound of ball bearing with one defect), Bull. JSME
1983;26(220):1791e8.
[13] Knezevic J. Reliability, maintainability and supportability engineering:
a probabilistic approach. McGraw Hill; 1993.
[14] Ben-Daya MS, Duffuaa AR. Handbook of maintenance management and
engineering. Springer Verlag London Limited; 2009.
[15] Pedregal DJ, Garcia Marquez FP, Roberts C. An algorithmic approach for
maintenance management. Annals of Operations Research 2009;166:
109e24.
[16] Campbell JD, Jardine AKS. Maintenance excellence: optimizing equipment
life-cycle decisions. New York: Marcel Dekker; 2001.
[17] Garcia Marquez FP. An approach to remote condition monitoring systems
management. The IET International Conference on Railway Condition
Monitoring; 2006:156e60.
[18] Garcia Marquez FP, Pedregal DJ, Roberts C. Time series methods applied to
failure prediction and detection. Reliability Engineering & System Safety
2010;95(6):698e703.
[19] Garcia Marquez FP, Roberts C, Tobias A. Railway point mechanisms: condition monitoring and fault detection. In: Proceedings of the Institution of
mechanical Engineers, Part F, Journal of Rail and Rapid Transit, vol. 224(1).
Professional Engineering Publishing; 2010. p. 35e44.
[20] Byon E, Ding Y. Season-dependent condition-based maintenance for a wind
turbine using a partially observed markov decision process. IEEE Transactions on Power Systems 2010;25(4):1823e34.
[21] McMillan D, Ault GW. Condition monitoring benefit for onshore wind
turbines: sensitivity to operational parameters. IET Renewable Power
Generation 2008;2(1):60e72.
[22] Moubray J. Reliability-centered maintenance. New York: Industrial Press;
1997.
[23] Smith AM. Reliability-centred maintenance. New York: McGraw-Hill, Inc;
1993.
[24] Garcia Marquez FP, Pedregal DJ. Applied RCM2 algorithms based on statistical methods. International Journal of Automation and Computing;
4:109e116.
[25] Deshpande VS, Modak JP. Application of RCM for safety considerations in
a steel plant. Reliability Engineering and System Safety 2002;3(78):325e34.
[26] Garcia Marquez FP, Schmid F, Collado JC. A reliability centered approach to
remote condition monitoring. A railway points case study. Reliability Engineering & System Safety 2003;80(1):33e40.
[27] Garcia Marquez FP, Schmid F, Collado JC. Wear assessment employing
remote condition monitoring: a case study. Wear 2003;2(255):1209e20.
[28] Deshpande VS, Modak JP. Application of RCM to a medium scale industry.
Reliability Engineering and System Safety 2002;1(77):31e43.
[29] Andrawus JA, Watson J, Kishk M, Adam A. The selection of suitable maintenance strategy for wind turbines. Wind Engineering 2009;30(6):471e86.
[30] Wiggelinkhuizen E, Verbruggen T, Xiang J, Watson SJ, Giebel G, Norton E,
et al. CONMOW: condition monitoring for offshore wind farms. In:
Proceedings of the 2007 EWEA European Wind Energy Conference
(EWEC2007), Milan, Italy; 7e10 May 2007.
[31] Scarf PA. A framework for condition monitoring and condition based maintenance. Quality Technology &. Quantitative Management 2007;4(2):301e12.
[32] Cozens NJ, Watson SJ. State of the art condition monitoring techniques
suitable for wind turbines and wind farm applications, report for CONMOW
project; 2003.
[33] Watson SJ, Infield DG, Xiang J. Condition monitoring of wind turbines e
Measurements and methods. IET Renewable Power Generation; 2008.
[34] Nilsson J, Bertling L. Maintenance management of wind power systems using
condition monitoring systems e Life Cycle costs analysis for two case
Studies. IEE Transactions on Energy Conversion 2007;22(1).
[35] Hameed Z, Hong YS, Choa YM, Ahn SH, Song CK. Condition monitoring and
fault detection of wind turbines and related algorithms: a review. Renewable
and Sustainable Energy Reviews 2009;13:1e39.
[36] Verbruggen TW. Wind turbine operation & maintenance based on condition
monitoring, WT-O. Final report, ECN-Ce03e047; April 2003.
[37] Caselitz P, Giebhardt J. Fault prediction techniques for offshore wind farm
maintenance and repair strategies. In: Proceedings of the EWEC2003; 2003.
[38] Caselitz P, Giebhardt J, Mevenkamp M. On-line fault detection and prediction
in wind energy converters. In: Proceedings of EWEC’94, Thessaloniki,
Greece; 1994. p. 623e7.
[39] Futter DN. Vibration monitoring of industrial gearboxes using time domain
averaging. In: IMechE 2nd International conference on gearbox noise,
vibration and diagnostics. Mechanical Engineering Publications; 1995.
[40] Lin ST, McFadden PD. Vibration analysis of gearboxes by the linear wavelet
transform. In: IMechE 2nd International conference on gearbox noise,
vibration and diagnostics. Mechanical Engineering Publications; 1995.
[41] McFadden PD. Examination of a technique for the early detection of failure in
gears by signal processing of the time domain average of the meshing
vibration. Mechanical Systems and Signal Processing 1987;1(2):173e83.
[42] McFadden PD. Detecting fatigue cracks in gears by amplitude and phase
demodulation of the meshing vibration, Journal of vibration, acoustics. Stress
and Reliability in Design 1996;108:165e70.
[43] Miller AJ, 1999. A New wavelet basis for the decomposition of gear motion
error signals and its application to gearbox diagnostics, Master of Science
Thesis, The Pennsylvania State University.
[44] Dyer D, Stewart RM. Detection of rolling element bearing damage by
statistical vibration analysis. ASME Journal of Mechanical Design 1978;100:
229e35.
[45] Sun Q, Tang Y. Singularity analysis using continuous wavelet transform for
bearing fault diagnosis. Mechanical Systems and Signal Processing 2002;
16(6):1025e41.
[46] Tse PW, Peng YH, Yam R. Wavelet analysis and envelope detection for rolling
element bearing fault diagnosis-their effectiveness and Flexibilities. ASME
Journal of Vibration and Acoustics 2001;123:303e10.
[47] Tandon N, Nakra BC. Vibration and acoustic monitoring techniques for the
detection of defects in rolling element bearings e a review. Shock and
Vibration Digest 1992;24(3):3e11.
[48] Tandon N, Choudhury A. A review of the vibration and acoustic measurement methods for detection of defects in rolling element bearings. Tribology
International 1999;32(8):469e80.
[49] Yoshioka T. Detection of rolling contact subsurface fatigue cracks using
acoustic emission technique. Lubrication Engineering 1992;49(4):303e8.
[50] Yoshioka T, Fuhjiwara T. Application of acoustic emission technique to
detection of rolling element-bearing failure. In: Dornfield DA, editor.
Acoustic Emission Monitoring and Analysis in Manufacturing. New York:
ASME; 1984. p. 55e75.
[51] Yoshioka T, Takeda M. Classification of rolling contact fatigue initiation using
acoustic emission technique. Lubrication Engineering 1994;51(1):41e4.
[52] Tandon N, Yadava GS, Ramakrishna KM. A comparison of some condition
monitoring techniques for the detection of defect in induction motor ball
bearings. Mechanical Systems and Signal Processing 2007;21(1):244e56.
[53] Tandon N, Nakra BC. Defect detection in rolling element bearings by acoustic
emission method. Journal of Acoustic Emission 1990;9(1):25e8.
[54] Tan CC. Application of acoustic emission to the detection of bearing failures.
In: Proceedings Tribology Conference, Brisbane; 1990. p. 110e4.
[55] Wei J, McCarty J. Acoustic emission evaluation of composite wind turbine
blades during fatigue testing. Wind Engineering 1993;17(No. 6):266e74.
[56] Giebel G, Oliver G, Malcolm M, Kaj B. Common Access to wind turbines data for
condition monitoring. Riso National Laboratory. In: Proceedings of the 27th
Riso International Symposium on Material Science, Denmark; 2006. p. 157e64.
[57] Blanch MJ, Dutton AG. Acoustic emission monitoring of field Tests of an
operating wind turbine. Key Engineering Materials 2003;245-246:475e82.
[58] Jungert A. Damage detection in wind turbine blades using two different
acoustic techniques. The e-Journal of Nondestructive Testing; 2008:25.
[59] Anastassopoulos AA, Kouroussis DA, Nikolaidis VN, Proust A, Dutton AG,
Blanch MJ, et al. Structural integrity evaluation of wind turbines blades using
pattern recognition analysis on acoustic emission data. Journal of Acoustic
Emission 2002;20:229e37.
[60] Joosse PA, Blanch MJ, Dutton AG, Kouroussis DA, Philippidis TP, Vionis PS.
Acoustic emission monitoring of small wind turbine blades. Journal of Solar
energy Engineering 2002;124(4):446e55.
[61] Endrenyi J, McCauley J, Singh C. The present status of maintenance strategies
and the impact of maintenance on reliability. IEEE Transaction Power System
2001;16(4):638e46.
[62] Castaings M, Cawley P. The generation, propagation and detection of Lamb
waves in plates using air-coupled ultrasonic transducers. Journal of Acoustic
Society of America 1996;100(5):3070e7.
[63] Castaings M, Cawley P, Farlow R, Hayward G. Single Sided inspection of
composite materials using air coupled ultrasound. Journal of Nondestructive
Evaluation 1998;17(1):37e45.
[64] Raghavan A, Carlos E, Cesnik S. Review of guided-wave structural health
monitoring. The Shock and Vibration Digest 2007;39:91e114.
[65] Tsai CS, Hsieh CT, Huang SJ. Enhancement of damage detection of wind
turbines. IEEE Transactions on Energy Conversion 2006;21(3):776e81.
[66] Juengert A, Grosse CU. Inspection techniques for wind turbine bladesusing
ultrasound and sound waves. In: Proceedings of non-destructive testing in
Civil Engineering, Nantes, France, 2009; 2009.
niene_ E, Zukauskas
[67] Raisutis R, Jasiu
E. Ultrasonic NDT of wind turbines blades
using guided waves. Ultrasound, vol. 63. Kaunas: Technologija; 2008. 7e11.
niene_ E, Raisutis R, Sliteris R, Voleisis A, Jakas M. Ultrasonic NDT of wind
[68] Jasiu
turbine blades using contact pulse-echo immersion testing with moving
water container. Ultragarsas (Ultrasound) 2008;63(3):28e32.
[69] Jasiuniene E, Raisutis R, Sliteris R, Voleisis A, Vladisauskas A, Mitchard D,
et al. NDT of wind turbine blades using adapted ultrasonic and radiographic
techniques. Insight- Non-Destructive Testing and Condition Monitoring
2009;51(9):477e83.
niene_ E, Sliteris
[70] Raisutis R, Jasiu
R, Vladisauskas A. The review of nondestructive testing techniques suitable for inspection of the wind turbines
blades. Ultragarsas (Ultrasound) 2008;63(1):26e30.
[71] Yin YH, Wang WH, Yan XP, Xiao HL, Wang CT. An integrated online oil
analysis method for condition monitoring. Measurement Science and Technology 2003;14(11):1973e7.
[72] Leske S, Kitaljevich D. Managing gearbox failure, Dewek. Dewi Magazine;
2006. N. 29.
[73] Barron T. Engineering condition monitoring. Addison Wesley Longman; 1996.
[74] Toms LA. Machinery oil analysis: methods, automation and benefits. Coastal;
1998.
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
[75] Morfiadakis E, Papadopoulos K, Philippidis TP. Assessment of the strain
gauge technique for measurement of wind turbine blade loads. Wind Energy
2000;3(1):35e65.
[76] Schroeder K, Ecke W, Apitz J, Lembke E, Lenschow G. A fibre Bragg grating
sensor system monitors operational load in a wind turbine rotor blade.
Measurement Science and Technology 2006;17(5):1167e72.
[77] Wernicke J, Shadden J, Kuhnt S, Byars R, Rhead P, Damaschke. Field experience of fibre optical strain sensors for providing real time load information
from wind turbine blades during operation. In: Paper presented at the
European wind energy conference, 22e25 November, London, UK; 2004.
[78] Schoen RR, Lin BK, Habetler TG, Schlag JH, Farag S. An unsupervised, on-line
system for induction motor fault detection using stator current monitoring.
IEEE Transactions on Industry Applications 1995;31(6):1280e6.
[79] Seo DC, Lee JJ. Damage detection of CFRP laminates using electrical resistance
measurement and neural network. Composite Structures 1999;47:525e30.
[80] Todoroki A, Tanaka Y. Delamination identification of cross-ply graphiteeepoxy composite beams using electric resistance change method.
Composites Science and Technology 2002;62:629e39.
[81] Matsuzaki R, Todoroki A. Wireless detection of internal delamination cracks
in CFRP laminates using oscillating frequency changes. Composites Science
and Technology 2006;66:407e16.
[82] Butler DE. The shock pulse method for the detection of damaged rolling
bearings. NDT International 1973;6(2):92e5.
[83] Zhen L, Zhengjia H, Yanyang Z, Xuefeng C. Bearing condition monitoring
based on shock pulse method and improved redundant lifting scheme.
Mathematics and Compuers in Simulation ;79(3):318e38.
[84] Tandon N, Nakra BC. Comparison of vibration and acoustic measurement
techniques for the condition monitoring of rolling element bearings.
Tribology International 1992;25(3):205e12.
[85] Morando IE. Measuring shock pulses is Ideal for bearing condition monitoring. Pulp and Paper 1988;62(12):96e8.
[86] Müller H, Pöller M, Basteck A, Tilscher M, Pfister J. Grid compatibility of
variable speed wind turbines with directly coupled synchronous generator
and hydro-dynamically controlled gearbox. In: Sixth International Workshop
on Large-Scale integration of wind power and transmission networks for
offshore wind farms, Delft, NL; 2006. p. 307e15.
[87] Zaher AS, McArthur SDJ. A multi-agent fault detection system for wind
turbines defect recognition and diagnosis. In: Proceedings IEEE Lausanne
POWERTECH 2007; 2007. p. 22e7.
[88] Sorensen BF, Lading L, Sendrup P, McGugan M, Debel CP, Kristensen OJD,
et al. Fundamentals for remote structural health monitoring of wind turbines
blades - A Preproject. Riso-R-1336(EN); 2002.
[89] Dale SL, Dolan, Lehn PW. Real-time wind turbine emulator suitable for
power quality and dynamic control Studies. In: Proceedings of international
conference on power systems transients, Montreal, Canada; 2005. p. 1e6.
[90] Peters ST. Handbook of composites. London: Chapman & Hall; 1998.
839e855.
[91] Fantidis JG, Potolias C, Bandekas DV. Wind turbine blade nondestructive
testing with a transportable Radiography system. Science and Technology of
Nuclear Installations; 2011. ID 347320.
[92] Smith BM. Condition monitoring by thermography. NDT International 1978;
11(3):121e2.
[93] Rumsey MA, Musial W. Application of infrared thermography nondestructive
testing during wind turbine blade Tests. Journal of Solar Energy Engineering
2001;123(4):271.
[94] Smith GM, Clayton BR, Dutton AG, Irving AD. Infra-red thermography for
condition monitoring of composite wind turbine blades feasibility studies
using cyclic loading tests. In: 15th British Wind Energy Association Conference (BWEA15), York, UK; 1993.
[95] Beattie AG, Rumsey M. Non-destructive evaluation of wind turbine blades
using an Infrared camera. In: ASME wind energy Symposium, 18th, Aerospace Sciences Meeting and Exhibit, 37th, Reno, NV; 1999.
ski L, Krawczuk M. Damage detection in turbine wind blades by
[96] Dolin
vibration based methods. In: 7th International conference on Modern
practice in stress and vibration analysis; 2009. Journal of Physics: Conference
series 181 012086.
[97] Levitin G. Genetic algorithms in reliability engineering. Reliability Engineering and System Safety 2006;91(9):975e6.
[98] Thiringer T. Power quality measurements performed on a low-voltage grid
equipped with two wind turbines. IEEE Transactions on Energy Conversion
1996;11:601e6.
[99] Tana CK, Irvinga P, Mba D. A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and
spectrometric oil analysis for spur gears. Mechanical Systems and Signal
Processing 2007;21:208e33.
[100] Garcia Marquez FP, Pedregal DJ. Failure analysis and diagnostics for railway
trackside equipment. Engineering Failure Analysis 2007;14(8):1411e26.
[101] Garcia Marquez FP, Pedregal DJ, Schmid F. Unobserved component models
applied to the assessment of wear in railway points: a case study. European
Journal of Operational Research 2007;176:1702e3.
[102] Llombart-Estopiña A. Improving the operation and maintenance of wind
farms: determination of wind turbines performance. In: Proceedings of
ICREPQ; 2008.
[103] Giebhardt J. Condition monitoring for wind turbines; 2007. State of the Art
Report As Deliverable 7.1.1, WP7 Condition monitoring. UPWIND.
177
[104] Cheng J, Yang Y, Yu D. The envelope order spectrum based on generalized
demodulation timeefrequency analysis and its application to gear fault
diagnosis. Mechanical Systems and Signal Processing 2010;24:508e21.
[105] Li CJ, Li SY. Acoustic emission analysis for bearing condition monitoring.
Wear 1995;185:64e74.
[106] Wismer NJ. Gearbox analysis using cepstrum analysis and comb liftering;
1994. Application Note Brüel & Kjaer, Denmark.
[107] Caselitz P, Giebhardt J, Mevenkamp M, Reichardt M. Application of condition
monitoring systems in wind energy converters. In: Proceedings of EWEC’97,
Dublin; 1997. p. 579e82.
[108] Zheng GT, Wang WJ. A new cepstral analysis procedure of recovering excitations for transient components of vibration signals and applications to
rotating machinery condition monitoring. ASME Journal of Vibration and
Acoustics 2001;123:222e9.
[109] Combet L, Gelman L. An automated methodology for performing time
synchronous averaging of a gearbox signal without speed sensor. Mechanical
Systems and Signal Processing 2007;21(6):2590e606.
[110] Bechhoefer E, Kingsley M. A review of time synchronous average algorithms.
In: Annual conference of the prognostics and Health management Society,
San Diego, CA; 2009.
[111] Burrus CS, McClellan JH, Oppenheim AV, Parks TW, Schafer RW,
Schuessler HW. Computer-based exercises for signal processing using
MATLAB. Englewood Cliffs, NJ: Prentice-Hall; 1994. 43e59.
[112] Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and
prognostics implementing condition-based maintenance. Mechanical
Systems and Signal Processing 2006;20(7):1483e510.
[113] Caselitz P, Giebhardt J. Rotor condition monitoring for improved operational
safety of offshore wind energy converters. Transactions of the ASME. Journal
of Solar Energy Engineering 2005;127(2):253e61.
[114] Yang WX, Tavner PJ, Wilkinson M. Condition monitoring and fault diagnosis
of a WT with a synchronous generator using wavelet transforms. In: The 4th
International conference on power electronics, machines and Drives 2008
(PEMD2008), York, UK; 2008. p. 6e10.
[115] Abbate A, DeCusatis CM, Das P. Wavelets and Subbands: fundamentals and
applications. Boston: Birkhauser; . 101e503.
[116] Newland DE. An introduction to random vibrations, spectral and wavelet
analysis. New York: Longman; 1993.
[117] Strang G, Nguyen T. Wavelets and filter banks. Wellesley-Cambridge Press;
1997.
[118] Staszewski WJ, Tomlinson GR. Application of the wavelet transform to fault
detection in a Spur gear. Mechanical Systems and Signal Processing 1994;8:
289e307.
[119] Wang WJ, McFadden PD. Application of wavelets to gearbox vibration signals
for fault detection. Journal of Sound and Vibration 1996;192:927e39.
[120] Luo GY, Osypiw D, Irle M. On-Line vibration analysis with fast continuous
wavelet algorithm for condition monitoring of bearing. Journal of Vibration
and Control 2003;9:931e47.
[121] Rubini R, Meneghetti U. Application of the envelope and wavelet transform
analyses for the diagnosis of incipient faults in ball bearings. Mechanical
Systems and Signal Processing 2001;15:287e302.
[122] Aretakis N, Mathioudakis K. Wavelet analysis for Gas turbine fault diagnostics. Journal of Engineering for Gas Turbines and Power 1997;119:
870e6.
[123] Peng ZK, Chu FL, Peter, Tse W. Singularity analysis of the vibration signals by
means of wavelet Modulus Maximal method. Mechanical Systems and Signal
Processing 2007;Vol. 21(2):780e94.
[124] Dalpiaz G, Rivola A. Condition monitoring and diagnostics in automatic
machines: comparison of vibration analysis techniques. Mechanical Systems
and Signal Processing 1997;11:53e73.
[125] Baydar N, Ball A. Detection of gear failures via vibration and acoustic signals
using wavelet transform. Mechanical Systems and Signal Processing 2003;
17:787e804.
[126] Miaoa Q, Makis V. Condition monitoring and classification of rotating
machinery using wavelets and Hidden Markov models. Mechanical Systems
and Signal Processing 2007;21:840e55.
[127] Xiang J, Watson SJ. Practical condition monitoring techniques for offshore
wind turbines. In: EWEC 2008 conference proceedings, Brussels, Belgium,
PO, vol. 195; 2008. p. 1e8.
[128] Kwon KC, Kim JH. Accident identification in nuclear power plants using
Hidden Markov models. Engineering Applications of Artificial Intelligence
1999;12:491e501.
[129] Atlas L, Ostendorf M, Bernard GD. Hidden Markov models for monitoring
machining tool-Wear. IEEE. ICASSP 2000;6:3887e90.
[130] Ocak H, Loparo KA. A New bearing fault detection and diagnosis Scheme based
on Hidden Markov modeling of vibration signals. IEEE ICASSP 2001;5:3141e4.
[131] Yam RCM, Tse PW, Li L, Tu P. Intelligent predictive decision support system
for condition-based maintenance. The International Journal of Advanced
Manufacturing Technology 2001;17(5):383e91.
[132] Garcia MC, Sanz-Bobi MA, del Pico J. SIMAP: intelligent system for predictive
maintenance application to the health condition monitoring of a windturbine gearbox. Computers in Industry 2006;57(6):552e68.
[133] Andrews JD, Moss TR. Reliability and risk assessment. Longmans; 1993.
[134] Ghoshal A, Sundaresan MJ, Schulz MJ, Pai PF. Structural health monitoring
techniques for wind turbines blades. Journal of Wind Engineering and
Industrial Aerodynamics 2000;85:309e24.
178
F.P. García Márquez et al. / Renewable Energy 46 (2012) 169e178
[135] Hong J-C, Kim YY, Lee HC, Lee YW. Damage detection using the Lipschitz
exponent estimated by the wavelet transform: application to vibration
modes of a Beam. International Journal of Solids and Structures 2002;Vol. 39:
1803e16.
[136] Robinson JC, Canada RG, Piety KR. Vibration monitoring on slow speed
machinery: new methodologies covering machinery from 0.5 to 600 RPM.
In: Proceedings of the Fifth International Conference on Profitable Condition
Monitoring Fluids and Machinery Performance Monitoring. UK: Mechanical
Engineering Publications Limited; 1996. p. 169e81.
[137] Becker E, Poste P. Keeping the blades turning: condition monitoring of wind
turbine gears. Refocus 2006;7(2):26e32.
[138] Sun Q, Tang Y, Yang W, Ji Y. Feature extraction with discrete wavelet transform
for drill wear monitoring. Journal of Vibration and Control 2005;11:1375e96.
[139] Nikolaou NG, Antoniadis IA. Rolling element bearing fault diagnosis using
wavelet packets. NDT & E International 2002;35(3):197e205.
[140] Gieske JH, Rumsey MA. Nondestructive evaluation (NDE) of composite/metal
bond interface of a wind turbines blade using an acousto-ultrasonic technique. In: Musial W, Berg DE, editors. ASME Wind Energy Symposium. AIAA/
ASME; 1997. p. 249e54.
[141] Beattie AG. Acoustic emission monitoring of a wind turbines blade during
a fatigue test. In: . Musial W, Berg DE, editors. ASME Wind Energy Symposium. AIAA/ASME; 1997. p. 239e48.
[142] Dutton AG, Blanch MJ, Vionis P, Lekou D, van Delft DRV, Joosse PA, et al.
Acoustic emission condition monitoring of wing turbine rotor blades: laboratory certification testing to large scale in-service deployment. In:
Proceedings of the 2001 European wind energy conference, Copenhagen,
Denmark; 2001.
[143] Menon S, Schoess JN, Hamza R, Busch D. Wavelet-Based acoustic emission
detection method with adaptive thresholding. In: Proceedings of the SPIE
3986; 2000. p. 71e7.
[144] Heng RB, Mohd MJ. Condition monitoring using acoustic signals. In:
Proceedings of the Fifth International Conference on Profitable Condition
Monitoring Fluids and Machinery Performance Monitoring. UK: Mechanical
Engineering Publications Limited; 1996. p. 145e58.
[145] Kim JS, Kang MC, Yyu BJ, Ji YK. Development of an online tool-life monitoring
system using acoustic emission signals in gear shaping. International Journal
of Machine Tools and Manufacture 1999;39(11):1761e77.
[146] Prabhakaran A, Jagga CR. Condition monitoring of Steam turbine-generator
through contamination analysis of used lubricating oil. Tribology International 1999;32:145e52.
[147] Van Paepegem W, Degrieck J. Simulating damage and permanent strain in
composites under in-plane fatigue loading. Computers and Structures 2005;
83:1930e42.
[148] Mba DU, Bannister RH. Profitable condition monitoring of low speed rotating
machinery using stress waves. In: Proceedings of the Fifth International
Conference on Profitable Condition Monitoring Fluids and Machinery
Performance Monitoring. UK: Mechanical Engineering Publications Limited;
1996. p. 159e68.
[149] Hatch C. Improved wind turbines condition monitoring using acceleration
enveloping. Orbit; 2004:58e61.
[150] Ness S, Sherlock CN. Nondestructive Testing Handbook. In: Nondestructive
Testing Overview, vol. 10. American Society for Nondestructive Testing; 1996.
[151] Drewry MA, Georgiou GA. A review of NDT techniques for wind turbines.
Insight 2007;49(3):137e41.