Engineering, 2013, 5, 948-955
Published Online December 2013 (
Open Access ENG
Vibration Signals and Condition Monitoring for Wind
Dimitrios Koulocheris, Georgios Gyparakis, Andonios Stathis, Theodore Costopoulos*
School of Mechanical Engineering, National Technical University of Athens, Athens, Greece
Email: *
Received October 8, 2013; revised November 8, 2013; accepted November 15, 2013
Copyright © 2013 Dimitrios Koulocheris et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Rolling element bearings are critical parts of modern wind turbines as they carry the loads of the turning structure and
the wind force. The stochastic nature of the wind loads makes it difficult to estimate the useful operational life of the
bearings. Condition monitoring of these bearings in a real time environment could be very helpful in estimating their
performance and in scheduling maintenance actions when a condition-based maintenance strategy is followed. This
procedure can be successfully implemented by using vibration analysis in the time domain or in the frequency domain,
giving useful results about the current condition of bearings and the location of potential faults. Permanently located
transducers on p roper positions on th e bear ings’ housings can b e used in order to collect, p rocess and evaluate real ti me
measurements and provide information about the bearing’s performance. In this work, a test rig is utilized in order to
evaluate the performance of rolling bearings. The results of the experimentation are satisfactory and the progress of
fatigue failures can be predicted through vibration analysis techniques showing that implementation in real scale may be
Keywords: Wind Turbines; Bearings; Basic Rating Life; Maintenance; Condition Monitoring; Vibration Signals
1. Introduction
In preventive maintenance, predetermined actions take
place between scheduled time intervals. Most of the time,
critical parts of machinery are replaced as they reach
their useful operational life, based on previous experi-
ence regarding their rated life. This maintenan ce strategy
often increases the total cost as the residual life of these
parts is wasted and some unnecessary preventive actions
take place. In order to minimize this cost, it is necessary
to schedule the p rev en tiv e mainten an ce action s acco rding
to the actual performance needs and operating co nditions
of each machine. The main aspects of this condition-
based maintenance approach are the proper diagnosis of
the current condition and the prognosis of its future evo-
Wind turbines are often installed at places which are
difficult to be reached. It would be helpful to establish a
condition monitoring system on these wind turbines in
order to evaluate the current condition and performance
of them and their major parts. Many diagnostic tools are
used in order to estimate and monitor the current condi-
tion of a machine part. One of these tools is vibration
signal analysis which is a largely used and effective tool
for the diagnosis of the current condition of rotating
equipment where the increased vibrations are indicative
for an abnormal performance.
In this paper, we experimentally evaluate vibration
signal analysis methods for rotating shafts and rolling
element bearings under various load and operating con-
ditions. The analysis can be performed in the time do-
main and/or in the frequency domain and a relationship
between the vibration signals and the evolution of a fail-
ure or wear is investigated.
2. Vibration Signal Analysis on Wind
Wind turbines often exhibit malfunctions due to shaft
misalignment, imbalance and bearing failures. These
failures are common in rotating equipment and they re-
sult in increased vibration levels. This vibration can be
measured close to its source, but often it can be detected
on other elements which are in contact. In this case in-
creased noise is affecting the signal [1-3].
*Corresponding author.
Displacement, velocity and acceleration transducers
can be used to extract these vibration signals. The most
common arrangement is the use of an accelerometer
which is permanently attached on the machine shell or
even better on the bearing housing. The accelerometer
can measure the excitation in one direction or even in
three if it is a tri-axial one. The signal is processed in
order to retrieve the desired characteristics and to reduce
the noise. This can be achieved with the use of appropri-
ate frequency filters (low-high pass filters, Wiener filter)
and the compensation of the systemic noise [4-7]. After
the initial signal processing, data can be further proc-
essed in the time or in the frequency domain.
2.1. Vibration Analysis in Time Domain
In time domain, data are processed with statistical and
numerical methods. The most common values that are
measured or calculated are [8,9]:
1) The minimum and the maximum amplitude (min-
2) The peak count.
3) Mean μ and standard deviation σ:
4) Root Mean Square (RMS):
rms i
5) The crest factor (peak to average).
6) Statistical moments of higher order such as skewness
S and kurtosis K:
2.2. Vibration Analysis in Frequency Domain
Certain faults and malfunctions exhibit signal peaks in
characteristic frequencies. Depending on the nature of
the faults, peaks occur at different characteristic frequen-
cies. The most common causes of these vibrations in the
case of wind turbines and their characteristic frequencies
are shown on Table 1.
Table 1. Wind turbine potential faults characteristic frequencies.
Cause/element Characteristic Frequency Remarks
Unbalance 1 f f = Shaft Rotation Frequency
Angular misalignment 1 f, lower at 2 f, 3 f
Parallel misalignment 2 f, lower at 1 f, 3 f
Bent shaft 1 f, lower at 2 f
Internal looseness 2 f, lower at its harmonics
Structure looseness 2 f, lower at its harmonics
Cage Rotating Frequency
Ball Pass Frequency of Inner Raceway
Ball Pass Frequency of Oute r Raceway
Bearings (rolling )
fd D
Ball Spin Frequency
f i
= Rotation Frequency of Gear i
GMF zf,1&2 i
Gear Mesh Frequency
HT zz
Hunting Tooth Frequen c y,
CF = common factors
Aerodynam ic vibra ti on s a
n = Number of Blades
, 2
Electric Line Frequency
2actual speed
ffP  Slip Frequency
Electrical faults
, 2
Pole Frequency
Natural component frequencies
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Data which have been recorded in the time domain are
can easily be represented in the frequency domain with
the use of a Fourier transformation. The most common
Fourier transform method is the Discrete Time Fourier
Transform (DTFT) which is calculated efficiently with
the use of a Fast Fourier Transform (FFT) algorithm. In
the frequency domain various types of analyses can be
performed such as spectral analysis, cepstrum analysis or
bispectral analysis [10].
Many researchers have shown satisfactory results in
diagnosing and predicting the evolution of bearing faults
using various vibration analysis techniques [11-15]. Most
of the times, the diagnosis of a fault is based on ob serva-
tions regarding changes in the measured characteristics
(peak counts, increase in magnitude, extreme variation).
Artificial intelligence (AI) and artificial neural networks
(ANN) are new areas of research [16-19]. ANNs are
trained to observe signals that correspond to an abnormal
operation. With the use of decision trees [20,21] and
fuzzy logic [22], various parameters are evaluated in or-
der to decide whether a failure has occurred.
Most studies regard experimental setups with mini-
mum distortion and low noise to affect the measured
signals. Laboratory con ditions and specific setups (where
the bearing under testing differs from the rest) help in
measuring the vibration signals produced by faults in
preliminary or advanced stages with low signal noise. In
most real life situations things are more complicated.
Vibrations and noise are introduced into the signal due to
the following reasons: angular and axial misalignment,
shaft imbalance, improper support and fixings, electrical
noise, transducer position, and load variation. These pa-
rameters are limiting the use of time domain analysis as
the magnitude of the measured signal is dominated
mostly by noise rather than the measured characteristic.
In these situations, frequency domain analysis seems to
present more useful results.
3. Experiments
3.1. Setup
The main components of a wind turbine are shown in
Figure 1. In the experimental setup a simplified model
has been created including the blades (simulated by an
applied weight force), the shaft, th e bearings of the shaft,
the motor/generator and the flexible coupling between
the motor and the shaft. For reasons of simplicity, other
major components of wind turbines, such as disk brakes
or gear boxes are not currently integrated on this setup.
The schematic of the bearing test rig on which the ex-
periments are carried out is shown in Figure 2.
In Figure 3, a photo of the test rig is shown. The rig
consists from a concrete platform based on four rubber
bumpers and a 30mm diameter shaft supported by two
Figure 1. Wind turbine major parts.
Figure 2. Test rig 3D model.
Figure 3. Photo of the test rig.
self-aligning double ball bearings in their housings. The
shaft is coupled with a 3-phase AC motor using a donut
type coupling arrangement, which can accommodate
small axial and angular misalignments and ensures that
the shaft is isolated and it is not affected by the motor’s
vibrations. The speed of the motor is controlled from a
variable speed drive and it can be adjusted between 0 -
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3000 rpm. The shaft is load ed using an arrangement con-
sisted from an 8 mm wired rope, a pulley and a bearing
with a flanged type of housing. An external load meas-
ured by a crane scale, forces the shaft radially down-
wards at its free end.
Three different types of bearings are used, having the
same bore diameter. This selection has been made in
order to use in each housing bearings with different
characteristic frequencies and as a result to have a clearer
indication about the source of the measured vibration
signal. All bearings have tapered internal rings with a
cylindrical bore diameter of 35 mm and they are mounted
on the shaft with the use of proper conical adapter
Two SKF type SNL 507 - 606 split type plummer
housings have been selected. The test bearings are a SKF
type 2207 EKTN9 double row self-aligning ball bearing
in the rear housing (towards the motor side) and a SKF
type 1207 EKTN9 double row self-aligning ball bearing
in the front housing.
One SKF type PF73 flanged housing is used in the ar-
rangement which is carrying the applied lo ad in conjunc-
tion with an SKF type YSA 207-2FK single row deep
groove ball Y-bearing which is sealed and greased for
Two Kistler type 8792A25T three-axial accelerome-
ters are mounted with magnets on the top of the two
plumper type housings. The accelerometers are capable
of measuring acceleration amplitudes up to 25 g. They
are connected to Kistler type 5134/5134B amplifiers and
their outputs are acquired by a HBM MGCplus data ac-
quisition machine.
Visualization and processing of the signals is made
through HBM CATMAN software installed on a Laptop
PC. The communication between the data acquisition
machine and the PC is made through a wired Ethernet
connection. Also, a K-type thermometer is used in order
to measure the shaft, bearings and housings temperatures
and the ambient temperature.
3.2. Measurements
The load applied to the shaft through the first flanged
housing bearing ranges from 0 to 2800 N. This load re-
sults in loading the first housed bearing (front) with a
range of 0 - 3500 N and the rear housed bearing with a
range of 0 - 700 N respectively. The motor speed ranges
from 0 to 3000 rpm.
Six channels are monitored and recorded, representing
the three axes of each accelerometer. The naming of the
axes is following the naming of the accelerometer’s axes
which is: x for the axis of the shaft, z for the axis of the
load direction and y for the axis vertical to the plane xz.
The sampling rate is set to 2400 samples per second
(sampling frequency) for each channel. Measurements
are conducted in different loading conditions and speeds
and they are monitored in real time as raw signal repre-
senting the overall acceleration measurement. The sig-
nals are also recorded for further processing and the du-
ration of each recorded measurement is set to 1 minute.
These data are recorded and saved in. BIN type files
which can be further processed with the aid of the appro-
priate software.
In one case, debris was added to the bearing lubricant
in order to cause an artificial time-progressing bearing
failure due to severe wear. This accelerated test aimed in
demonstrating the effectiveness of the proposed condi-
tion monitoring method.
4. Results and Discussion
During the experiment various operating condition s were
investigated regarding the rotational speed and the ap-
plied load. In these conditions it is obvious that the am-
plitude of the signal depends on the load. High loads and
high speeds cause increased vibration amplitudes, which
becomes even higher when a malfunction occurs and it
also depends on the severity of th e malfun c tion.
The measured signals are processed in time domain
and in frequency domain. In time domain, changes in
amplitude, peaks and rms values can be seen, but it is
difficult to identify the root cause of these changes.
Processing the values of the statistical variables (Equa-
tions (1)-(5)) does not give any useful results about the
source of the vibrations and so the malfunctions of the
system cannot be identified. The significance of the
overall vibration amplitude is of great importance as the
progress of the failure is visible and can trigger some
alarms if appropriate levels are set. From the plotted data
in Figure 4 it is shown that a failure in the rig can cause
significant increase in the measured vibration signals.
Processing the acquired data in the frequency domain
results in the identification of the true source of the vi-
brations and in the detection of possible faults. The
characteristic frequencies of the bearing, as they are
given by the manufacturer SKF, for a rotational speed of
2400 rpm are shown on Table 2. If a failure occurs in a
Table 2. Frequencies of potential damage in the tested ball
Frequency SKF 1207
EKTN9 ‘front’
SKF 2207
EKTN9 ‘rear’
207 - 2FK ‘load ’
f 40.0 Hz 40.0 Hz 40.0 Hz
fc 16.8 Hz 15.8 Hz 15.8 Hz
fbpfo 251.0 Hz 190.0 Hz 143.0 Hz
fbpfi 349.0 Hz 290.0 Hz 217.0 Hz
fr 119.0 Hz 89.1 Hz 92.1 Hz
2 x fr 238.0 Hz 178.0 Hz 184.0 Hz
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bearing, the amplitude of the relevant frequency is ex-
pected to increase. The frequencies for potential damages
on the cage, the inner raceway, the outer raceway and on
the balls respectively are fc, fbpir , fbpir, 2xfr respectively.
Spectral vibration analysis can show the cause of this
increase amplitude by examining the behavior of the am-
plitude of each characteristic frequency band.
In order to reduce the spectral leakage and make the
dominant frequencies easier to be identified, the power
spectrum is used, where the outputs of the FFT are raised
to the square.
In our case, a failure on the outer raceway can be iden-
tified as there is a peak on the vibration amplitude at the
frequencies around 250 Hz.
The failure is in its initial stage after 1 hour of opera-
tion (Figure 5) but even then a peak on the characteristic
frequency fbpfo is visible. After 12 hours of operation the
characteristic frequency fbpfo is much more dominant and
its amplitude has been increased dramatically. As it can
be seen in Figure 6 the amplitudes of some frequencies
have also been increased but they are relatively low
compared to the amplitude of the frequency correspond-
ing to a failure on the outer ring.
These snapshots of the frequency spectrum can be
continuous with time intervals relevant to the PC’s data
processing capability. Series of such data can be plotted
in a tridimensional plot style showing th e progress of the
amplitude in each characteristic frequency. A sample of
such a plot is shown in Figure 7 where 5 samples are
02 46 810 12 14
Time (hours)
Acceleration rms (g)
Acceleration rms x axis
Figure 4. RMS value of acceleration on x axis of the “front” bearing during the experiment.
0100 200 300 400 500 600
X: 119.8
Y: 0.005348
X: 319.6
Y: 0.01006
Frequency Spectrum x axis
Frequency (Hz)
Amplitude (g
X: 250.6
Y: 0.03787
X: 359.5
Y: 0.009028
Figure 5. Frequency spectrum of the “front” bearing after 1 hour of operation.
0100 200 300 400 500 600
X: 388.7
Y: 0.112
Frequency (Hz)
Frequency Spect r um x axis
Amplitude ( g
X: 251
Y: 1.588
Figure 6. Frequency spectrum of the “front” bearing after 12 hours of operation.
020 40 60 80 100 120 140
100 200 300 400 500 600
Time (ten samples per hou r)
X: 140
Y: 251.2
Z: 4.657
X: 100
Y: 251.3
Z: 1.945
Frequency Spectrum x axis
X: 60
Y: 249
Z: 0.2986
X: 20
Y: 250.4
Z: 0.04246
Frequency (Hz)
Amplitude (g
Figure 7. Evolution of the frequency spectra of the “front” bearing during the exper iment.
The progress of the failure on the outer raceway is
clearly seen. Starting from a rather low level until the
60th sample (after 6 hours of operation), the vibrations at
about 250 Hz are escalating reaching very high levels.
These findings are in conformity with the rms value of
acceleration in the time domain of the overall vibrations
but in the frequency domain additional information can
be extracted as the position of the fault on the bearing
and on the test rig can be clearly identified.
The results of the experimental setup can be summa-
rized in these points:
1) Vibration analysis in both time and frequency do-
mains can give useful information about the operating
conditions of wind turbines
2) Analysis in the frequency domain can be helpful in
identifying the source of the increased vibrations
3) An almost real time monitoring in the frequency do-
main is possible when continuous FFT transforms of
the measured signal are plotted
4) The amplitude of the signal of the potential damage
frequencies increases as the failure progresses
5. Conclusions
Vibration monitoring is playing an increasingly impor-
tant role as a tool for assisting predictive and preventive
maintenance and for improving the operation efficiency
and reliability of a plant throu gh the design maintenance.
When knowing the failure modes of a machine, it is pos-
sible to determine its condition and identify potential
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failures. This can be easily performed by analyzing the
signal in the frequency domain and comparing it with the
theoretical frequencies of potential failures.
In this experimental setup, vibration signals are moni-
tored through a data collectio n machine with an Ethernet
connection to a PC. Processing in the frequency domain
is causing a time lag but this is not critical for identifying
a potential progressing fault. The results of the tests show
that the implementation of a vibration monitoring system
on a wind turbine can be helpfu l in identifying and moni-
toring an occurring failure.
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