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Detection of an antifriction bearing faults is one of the most challenging tasks in bearing health condition monitoring, especially when the fault is at its initial stage. The defects in bearing unless detected in time may lead to malfunctioning of the machinery. The defects in the rolling element bearings may come up mainly due to the following reasons; improper design of the bearing or improper manufacturing or mounting, misalignment of bearing races, unequal diameter of rolling elements, improper lubrication, overloading, fatigue and uneven wear. This paper presents a detailed of the different detection techniques used for measuring rolling bearing defects. From in depth study, four different methods for detection and diagnosis of bearing defects; they may be broadly classified as vibration measurements, acoustic measurements, temperature measurements and wear debris analysis have been identified. It is observed that the vibration analysis is most commonly accepted technique due to its ease of application.
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Bearing Fault Detection Techniques - A Review
Nabhan, A.1*, Nouby, M.2, Sami, A.M.1, Mousa, M.O.1
1Production and Design Dept., Faculty of Engineering, Minia University, Minia 61111, Egypt
2Mechanical Engineering Dept., Faculty of Engineering, South Valley University, Qena-83521, Egypt
*Corresponding author E-mail: a.nabhan@mu.edu.eg.
Abstract
Detection of an antifriction bearing faults is one of the most challenging tasks in bearing health
condition monitoring, especially when the fault is at its initial stage. The defects in bearing unless
detected in time may lead to malfunctioning of the machinery. The defects in the rolling element
bearings may come up mainly due to the following reasons; improper design of the bearing or improper
manufacturing or mounting, misalignment of bearing races, unequal diameter of rolling elements,
improper lubrication, overloading, fatigue and uneven wear. This paper presents a detailed of the
different detection techniques used for measuring rolling bearing defects. From in depth study, four
different methods for detection and diagnosis of bearing defects; they may be broadly classified as
vibration measurements, acoustic measurements, temperature measurements and wear debris analysis
have been identified. It is observed that the vibration analysis is most commonly accepted technique due
to its ease of application.
Keywords: Bearing Defects, Vibration Measurements, Acoustic Measurements, Temperature Measurements,
Wear Debris Analysis.
1. Introduction
In the recent years, condition monitoring and fault diagnosis of equipment are of great concern in
industries. Early fault detection in machineries can save millions of dollars in emergency maintenance
costs. In rotating machinery, rolling element bearings are one of the most critical components because
they are the most commonly wearing parts and a large majority of system failures arise from faulty
bearings. Proper functioning of these machine elements is extremely important in industry in order to
prevent long term, costly catastrophic downtimes. A reliable online machinery condition monitoring
system is very useful to a wide array of industries to recognize an incipient machinery defect so as to
prevent machinery performance degradation, malfunctions, or even catastrophic failures [1]. It was
obvious that more attention must be paid to the condition of a rolling element bearing if the human life
is in question. Thus, advanced technologies are needed to monitor the health status of bearings
efficiently and effectively [2].
Generally, bearings can basically be categorized as two types, sliding bearings and rolling bearings.
Sliding bearings includes linear bearings and journal bearings. Ball and roller bearings, together called
rolling bearings, are commonly used machine elements. Rolling element bearings are machine elements
that permit rotary motion of shafts in in machinery for a wide range of applications such as bicycles,
roller skates, electric motors, aircraft gas turbines, rolling mills, dental drills, gyroscopes, and power
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transmissions [3]. Compared with other types of bearings, rolling bearings have many advantages. They
are often referred to as antifriction bearings because they require a small amount of lubrication [1].
Rolling bearings can operate with low friction and they are suitable for high-speed shaft speeds that
demand outstanding endurance. Actually, rolling bearings are consists of different parts: an outer race,
an inner race, rolling elements that are in contact under heavy dynamic loads and relatively high speeds,
and optionally a cage around these rolling elements, as shown in Figure 1. A detailed introduction to
rolling element bearings is available in [4-6].
Faults may occur in any of these parts, and often these faults are single point defects such as chips or
dents [7]. As these elements move past each other, these defects come into periodic contact with other
elements in the bearing, and at each contact they can excite a high frequency resonance in the overall
structure. Rolling bearing damage may result in a complete failure of the rolling bearing at least,
however, in a reduction in operating efficiency of the bearing arrangement. Only if operating and
environmental conditions as well as the details of the bearing arrangement are completely in tune, can
the bearing arrangement operate efficiently. Bearing damage does not always originate from the bearing
alone. Damage due to bearing defects in material or workmanship is exceptional [8].
In the recent years, manufacturing community has been concentrating on finding out various techniques
and methodologies in order to improve the bearing designs. However, the large number of bearings
associated with any given process increases the likelihood of system failure. Researches are using
various approaches like mathematical models, computer aided engineering (CAE) based simulation
models and experimental models. The main purpose of this paper is to present a detailed of the different
measurement techniques in the last period on bearing defects. From in depth study, four different
methods for detection and diagnosis of bearing defects; they may be broadly classified as vibration
measurements, acoustic measurements, temperature measurements and wear debris analysis have been
identified.
Fig. 1 Main components of the rolling bearing (SKF).
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2. Vibration Measurements
Vibration analysis is among the most common method used in the monitoring applications since a defect
produces successive impulses at every contact of defect and the rolling element, and the housing
structure is forced to vibrate at its natural modes. The vibration pattern of a damaged bearing includes
the low-frequency components related to the impacts and the high-frequency components. The structural
information of the bearing structure or the machine is stored.
The use of the statistical moments of the rectified data was proposed and compared to the moment of the
original data, [19]. Simulation analysis studies the effects of the disturbing signals on different
parameters. Simulating the development of a bearing provides a good understanding of the behavior of
the statistical parameters which are going to be used for condition monitoring of bearing.
The statistically efficient method is used in frequency estimation of envelope-autocorrelation from the
fault rolling element bearing under very low shaft speed and light load. By using the estimated
frequency, a simple notch filter removes the frequency component so that further detail in the vibration
signal may be analyzed, [20]. In this instance, the fault characteristic frequency and its harmonics are
estimated and subsequently removed with this technique. The vibration data captured and used for
determination and validation is composed from four different defects states of the rolling element
bearing -outer raceway defect, inner raceway defect, ball defect, and combination of the bearing
elements defect- and one representing normal state of the bearing for four different running speeds with
two load levels, [21].
An alternative framework for analyzing bearing vibration signals, based on cyclostationary analysis, was
proposed, [22]. The degree of cyclostationary function can provide a first overall indication of the
appearance of several distinct modulating frequencies. The basic concepts of the approach are
demonstrated both in illustrative simulation results, as well as in experimental results and industrial
measurements for different types of bearing faults.
The synchronous averages are used to examine the calculation of the envelope signal of the high-
frequency vibration produced by rolling element bearings with spalling damage, [23]. By synchronizing
with the rotation speed of the shaft relative to the cage, an estimate is obtained of the distribution of the
damage on the inner race of the bearing, and of the variations between rolling elements. The techniques
are demonstrated by experiments on a laboratory test rig with a rolling bearing under radial load and
multiple simulated spalls on the inner race.
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The effect of local defects on the nodal excitation functions is modeled. Simulated vibration signals are
obtained, [24]. The signal analysis is applied to the simulated signals to obtain an indicator value for the
defect. RMS values are obtained in the time domain and the high frequency resonance technique is used
in the frequency domain. The indicator values are obtained for various rotational speeds, for different
loading and housing structure. The indicator value from different signal processing methods which is
most sensitive to the defect is found for different cases.
Estimation of the running speed and the bearing key frequencies are required for failure detection and
diagnosis. Experimental data were used to verify the validity of the algorithms. Data were collected
through an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor.
Both inner and outer race defects were artificially introduced to the bearing using electrical discharge
machining, [25]. A linear vibration model was also developed for generating simulated vibration data.
The simulated data were also used to validate the performance of the algorithms.
A complex filter for Hilbert transform is proposed to apply in the real-time vibration signal
demodulation. The filter could provide a complex signal directly, as a function of both frequency and
time, and the envelope could be derived from the absolute value of the complex signal. Three
parameters, the scaling factor, center frequency and pass band width, are designated to achieve the
satisfactory properties of fast waveform convergence, constant pass band gain and little phase distortion.
Thus, a finite waveform interval of the proposed filter could be possibly applied in the vibration signal
demodulation, [26]. From theoretical analysis and experimental studies, it is shown that the proposed
filter could be effectively applied in the real-time vibration signal demodulation for a roller bearing
system.
Dynamic loading model is proposed to model the localized rolling element bearing defects. Statistical
properties of the vibration signals for healthy and defected structures are compared, [27]. The envelope
high frequency resonance technique method is employed in the frequency domain analysis. The effect of
the rotational speed on the diagnostics of rolling element bearing defects is investigated. An optimum
sensor location on the structure is sought. Effect of the structure geometry on the monitoring techniques
is studied.
Time domain analysis, frequency domain analysis and spike energy analysis have been employed to
identify different defects in bearings, [28]. Spike energy factor helps to identify the severity of the defect
in antifriction bearings. The distinct and different behavior of vibration signals from bearings with inner
race defect, outer race defect and roller defect helps in identifying the defects in roller bearings. The
5
results have demonstrated that each one of these techniques is useful to detect problems in antifriction
bearings.
The machine is a single stage centrifugal compressor with a rolling element thrust bearing on the motor
free end and a sleeve bearing on the motor drive end. The expert system severity score is an excellent
way to consistently trend the bearing health because it always applies the same logic and looks at a
number of features in the data. The increase of the severity towards the extreme level and a bearing
replacement is ordered. The defect was detected using off the shelf portable vibration analysis hardware
and software, [29].
The partial correlation integral algorithm is used to analyze machine vibration data obtained throughout
a life test of a rolling element bearing. The dimensional exponent computed from the partial correlation
integral algorithm tends to increase as time progresses and the useful remaining life of the bearing is
decreasing, [30]. The dimensional exponents of a healthy bearing and a bearing close to failure are
statistically different. As a result, the condition of the bearing can characterized from the results of the
surrogate data test. Furthermore, the dimensional exponent can be used to predict the failure of rolling
element bearings in rotating machinery from real-time vibration data.
A dynamic simulation method is proposed to study ball bearing with local defect based on the coupling
of the piecewise function and the contact mechanism at the edge of the local defect, [31]. The ball
bearing is modeled as a two-degree of freedom system. The impulse force is determined by the ratio of
the ball size to the defect size and the contact deformation at the edge of the local defect is included. The
proposed method can provide a more close to real impulse for the contact between the ball and the race
with different defect sizes compared to the assumed rectangular or half-sine impulse function.
The localized bearing defects in spindles were modeled visually. The vibration responses generated due
to the outer ring defect were simulated. The finite element model of the spindle is capable of predicting
the acceleration time responses due to the excitation, [32]. The noise decreases the amplitudes at the
bearing characteristic frequencies in the envelop spectrum.
The detection of local defects existing on races of deep groove ball bearing was investigated using
envelope analysis and Duffing oscillator. Experiments have been carried out using a test rig for
capturing the vibration signals of test bearing. The external vibration has been imparted to the housing
of the test bearing through electromechanical shaker, [33]. The Duffing oscillator has acquired the weak
defective bearing signal. The close phase plane trajectories of Duffing oscillator have confirmed the
presence of the defects on the races of the test bearing.
6
The scalar parameters, peak-to-peak value, RMS, Crest factor and kurtosis, show damage at the ball
bearing but they do not give information about the location of defect. Therefore, spectrum analyses are
conducted at specified test durations in order to predict defect locations, [34]. Vibration signatures
produced are recorded and statistical measures are calculated during the test. Vibration spectra are
obtained and examined to determine where the defect is on the running surfaces.
The vibration signals obtained from an accelerometer were measured and analyzed for comparative
purposes. The time-domain statistical parameters and frequency-domain modified Peak Ratio
were calculated and compared. The study revealed that ultrasound technique is demonstrably superior
to vibration acceleration measurements for detecting incipient defects in low speed bearings, [35]. The
RMS of ultrasound signals provided the best parameter at almost all speeds. However, at very low
speeds, the kurtosis and crest factors performed best. In the frequency domain, a modified Peak Ratio
was proposed and was proven to a better indicator than the original Peak Ratio.
A simple time series method for bearing fault feature extraction using singular spectrum analysis of the
vibration signal is proposed, [36]. The algorithms were evaluated using two experimental data sets one
from a motor bearing subjected to different fault severity levels at various loads, with and without noise.
The effect of sample size, fault size and load on the fault feature is studied. The experimental results
demonstrate that the proposed bearing fault diagnosis method is simple, noise tolerant and efficient.
A finite element contact mechanics bearing model is established based on a contact algorithm suited to
high-precision elastic, [37]. The computational model includes all the important bearing details besides
basic geometry, such as, internal clearance, roller and race crowning, race width and thickness, and
dimensions of the raceway shoulders. The computed stiffness matrix captures the coupling between
radial, axial, and tilting deflections of rolling element bearings. The proposed stiffness determination
method is validated the experiments and compared to existing analytical models. A method is presented
for calculating and analyzing the quasi-static load distribution and varying stiffness of a radially loaded
double row bearing with a raceway defect of varying depth, length, and surface roughness, [38].
The resonance frequency in the first vibration mode of mechanical system was studied, [39]. Under the
assumption of a stepwise function for the envelope signal, the modulated signal could be decomposed
into a sinusoidal function basis at the first vibration mode resonance frequency. According to the
experimental study, the envelope detection method for the first vibration mode resonance frequency
could be effectively applied in the signal processing for the bearing defect diagnosis.
7
A mathematical model for the ball bearing vibrations due to defect on the bearing race has been
developed, [40]. It is found that, the amplitude level of vibrations for the case of outer race defect is
more than that for the inner race defect and the ball defect. The defect present on the inner race moves in
and out of the load zone during each revolution of the shaft. In this instance, the strong fault signatures
produced while the defect is in the load zone are averaged with the weaker signatures acquired while the
defect is outside the load zone. The theoretical model was aimed to study the effect of defect size, load
and speed on the bearing vibration and predict the spectral components.
Finite element model can be effectively used to differentiate between vibration signatures for defects of
different sizes in the bearing, [41]. Assumptions have been made for the variation of forces exerted by
the rolling element on the outer ring in the vicinity of the defect. Experiment result has been taken for
the analysis of the signal that has been obtained through the use of FFT analyzer. The vibration signal
response of the defected bearing is analyzed and compared with the normal bearing. The vibration signal
pattern obtained from the simulation was found to have similar characteristics with experimental data.
A dynamic loading model simulates the distribution load in the outer race due to transfer load from the
ball. Time domain analysis is performed to evaluate the output result of vibration analysis from the finite
element software. RMS and peak to peak value is used as the time signal descriptors and can be used as
a parameter for condition monitoring purposes. The vibration response of healthy and defected bearing
is compared. The simulated vibration pattern has similar characteristics with results from experimental
results, [42]. The effect of shaft rotational speed and radial load is investigated. The finite element
model of a bearing and housing structure has been developed. Then the model is analyzed to obtain the
vibration signal in the frequency domain, [43].
An analytical model is proposed to study the nonlinear dynamic behavior of rolling element bearing
systems including surface defects, [44]. Various surface defects due to local imperfections on raceways
and rolling elements are introduced to the proposed model. Mathematical expressions were derived for
inner race, outer race and rolling element local defects. The validity of the proposed model verified by
comparison of frequency components of the system response with those obtained from experiments.
The vibrations generated by deep groove ball bearings having multiple defects on races was studied,
[45]. The vibrations are analyzed in both time and frequency domains. The equations for time delay
between two or more successive impulses have been derived and validated with simulated and
experimental results. The relationships between amplitudes of frequencies for impulse train, delayed
8
impulse train and combination of two impulse trains have been established. Frequency spectra for single
and two defects on either race of deep groove ball bearings are compared.
Wavelet transform provides a variable resolution time-frequency distribution from which periodic
structural ringing due to repetitive force impulses, generated upon the passing of each rolling element
over the defect, were detected, [46]. A basic wavelet considered optimal for bearing localized defect
detection is constructed. Finally, the scheme is described and its effectiveness is evaluated using actual
vibration signals measured from bearings with defects at different locations and operating under
different conditions.
The discrete wavelet transform can be used as an effective tool for detecting single and multiple faults in
the ball bearings, [47]. Furthermore, discrete wavelet transform has been proposed for measuring outer
race defect width of taper roller bearing. Experiments were carried out on a customized test setup, [48].
Vibration signals from ball bearings having single and multiple point defects on inner race, outer race,
ball fault and combination of these faults have been considered for analysis, [49]. Wavelet transform
provides a variable resolution timefrequency distribution from which periodic structural ringing due to
repetitive force impulses, generated upon the passing of each rolling element over the defect, are
detected. The decomposed signal evidently splits the peak corresponding to the ball entry into and exit
from the fault, enabling in an estimation of the defect size present in the bearing, [50]. Experiments
conducted for different sizes of the defect present on the outer race of deep groove ball bearing affirm
the efficacy of the applied technique for different vibration signals. The output of the proposed
technique finds close correlation with the actual defect size measured from optical microscope
Wavelet Packet Analysis is a much better option than discrete wavelet transform. The method is
designed in such a way that it can exploit the underlying physical concepts of the modulation
mechanism, [51]. In Wavelet Packet Analysis, both the details and the approximation are decomposed
into lower level, resulting in a wavelet packet decomposition tree. The experimental results indicate that,
the wavelet packet analysis is a very reliable time-frequency domain approach capable of capturing high
frequency transients in bearing signals, [52]. Wavelet Packet Analysis is used as a powerful diagnostic
method for the detection of initial bearing failures via stator current analysis, [53]. The presented
method is evaluated using experimental signals. Sets of data are gathered before and after using
defective bearings. Compared to conventional methods, the superiority of the proposed method is shown
in the success of fault detection.
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Continuous wavelet transform is implemented to generate the scalogram. The generated scalogram is
used to identify and measure the seeded defects in bearing and gears, [54]. The designed adaptive
wavelet is used to compute the Continuous wavelet transform coefficients. The Continuous wavelet
transform coefficients so generated are compared with the standard wavelet based scalogram. The
adaptive wavelet transform is shown to be apposite in analyzing the vibration signals and also is
corroborated with the acquired experimental data. The scalogram generated from Continuous wavelet
transform is used to measure the time duration that the roller takes to roll over the defect. The vertical
strips drawn on the ridge spectrum corroborates well with defect width. It found that, the proposed
method can be reckoned suitable and reliable in measuring bearing defect width in real-time from
vibration signal, [55].
1.1.1 Acoustic Measurements
Acoustic emission is the phenomenon of transient elastic wave generation due to a rapid release of strain
energy caused by a structural alteration in a solid material under mechanical or thermal stresses, [56].
Generation and propagation of cracks, growth of twins, etc. associated with plastic deformation are
among the primary sources of Acoustic emission. Hence, it is an important tool for condition monitoring
through non-destructive testing.
Acoustic emission instrumentation consists of a transducer, mostly of the piezoelectric type, a
preamplifier and a signal processing unit. The transducers, which have very high natural frequency, have
a resonant type response. The bandwidth of the Acoustic emission signal can also be controlled by using
a suitable filter in the preamplifier. The advantage of acoustic emission monitoring over vibration
monitoring is that the former can detect the growth of subsurface cracks, whereas the latter can detect
defects only when they appear on the surface.
A bearing condition monitoring technique based on processing the acoustic emissions of the monitored
bearing was investigated, [57]. A bearing was mounted on a mechanical platform which allowed the
adjustment of the following three parameters; the thrust load, the angular misalignment, and the position
of the center of the bearing. Based on the experimental data of seeded bearing defects, acoustic
emissions is found to be a better signal than vibrations when the transducers have to be remotely placed
from the bearing.
10
Defects were simulated in the roller and inner race of the bearings by the spark erosion method.
Acoustic emission of bearings without defect and with defects of different sizes has been measured,
[58]. For small defect sizes, ring down counts of acoustic emission signal has been found to be a very
good parameter for the detection of defects both in the inner race and roller of the bearings tested.
However, the counts stopped increasing after a certain defect size. Distributions of events by ring down
counts and peak amplitudes are also found to be good indicators of bearing defect detection. With a
defect on a bearing element, the distributions of events tend to be over a wider range of peak amplitudes
and counts.
Comparisons between acoustic emission and vibration analysis over a range of speed and load
conditions were presented, [59]. An experimental test rig was designed such that defects of varying sizes
could be seeded onto the outer race of a test bearing. The primary source of acoustic emission activity
from seeded defects is investigated, in addition to determining the relationship between defect sizes. The
second test program aimed to establish a correlation between acoustic emission activities with increasing
defect size. It is concluded that, the acoustic emission offers earlier fault detection and improved
identification capabilities than vibration analysis. Furthermore, the acoustic emission technique also
provided an indication of the defect size, allowing the user to monitor the rate of degradation on the
bearing.
The acoustic emission acquisition system consisted of a piezoelectric type sensor fitted onto the top half
of the bearing housing. An increase in defect size resulted in an increase in levels of acoustic emission
energy for outer and inner race seeded defects. A correlation between the geometric size of outer race
defects and the acoustic emission bursts associated with such defects has been shown, [60]. It is
concluded that, the geometric defect size of outer race defects can be determined from the acoustic
emission waveform.
The effectiveness of acoustic emission technology in detecting and monitoring the initiation and
propagation of cracks was investigated, [61]. To undertake this task a special purpose test rig was built
that allowed for accelerated natural degradation of a bearing race. The results of classical Fast Fourier
Transformer of measured acoustic emission data are compared with three non-linear power spectral
estimation methods. It is concluded that sub-surface initiation and subsequent crack propagation can be
detected using a range of data analysis techniques on acoustic emission’s generated from natural
degrading bearings.
11
Cyclostationarity is a relatively new technique that offers diagnostic advantages for analysis of
vibrations from defective bearings. Similarly the Acoustic Emission technology has emerged as a viable
tool for preventive maintenance of rotating machines. The cyclic spectral correlation, a tool dedicated to
evidence the presence of Cyclostationarity, was compared with a traditional technique, the envelope
spectrum. The comparison showed that, the cyclic spectral correlation was most efficient for small
defect identification on outer race defects though the success was not mirrored on inner race defects. It is
concluded that, its offers better sensitivity to the continuous monitoring of defects compared to the use
of traditional temporal indicators, [62].
A new method is able to identify localized defects in an incipient stage, in which the signal-to-noise
ratio (SNR) is extremely low. This method combines Wavelet packet, for acoustic emission signal, the
Hilbert Transform (HT) for envelope extraction and autocorrelation function, to find patterns in the
acoustic emission signal. An extensive experimental investigation was carried out in order to evaluate
the performance of the proposed method under extremely low SNR, adding high level of noise to the
signals. The results indicate that the proposed enhanced envelope method is able to detect incipient
defects with 9 dB lower SNR than traditional envelope analysis, [63].
3. Acoustic Measurements
Acoustic emission is the phenomenon of transient elastic wave generation due to a rapid release of strain
energy caused by a structural alteration in a solid material under mechanical or thermal stresses, [44].
Generation and propagation of cracks, growth of twins, etc. associated with plastic deformation were
among the primary sources of Acoustic emission. Hence, it is an important tool for condition monitoring
through non-destructive testing. Acoustic emission instrumentation consists of a transducer, mostly of
the piezoelectric type, a preamplifier and a signal processing unit. The transducers, which have very
high natural frequency, have a resonant type response. The bandwidth of the Acoustic emission signal
can also be controlled by using a suitable filter in the preamplifier. The advantage of acoustic emission
monitoring over vibration monitoring is that the former can detect the growth of subsurface cracks,
whereas the latter can detect defects only when they appear on the surface.
A bearing condition monitoring technique based on processing the acoustic emissions of the monitored
bearing was investigated, [45]. A bearing was mounted on a mechanical platform which allowed the
adjustment of the following three parameters; the thrust load, the angular misalignment, and the position
of the center of the bearing. Based on the experimental data of seeded bearing defects, acoustic
emissions is found to be a better signal than vibrations when the transducers have to be remotely placed
from the bearing.
12
Defects were simulated in the roller and inner race of the bearings by the spark erosion method.
Acoustic emission of bearings without defect and with defects of different sizes has been measured,
[46]. For small defect sizes, ring down counts of acoustic emission signal has been found to be a very
good parameter for the detection of defects both in the inner race and roller of the bearings tested.
However, the counts stopped increasing after a certain defect size. Distributions of events by ring down
counts and peak amplitudes were also found to be good indicators of bearing defect detection. With a
defect on a bearing element, the distributions of events tend to be over a wider range of peak amplitudes
and counts.
Comparisons between acoustic emission and vibration analysis over a range of speed and load
conditions were presented, [47]. An experimental test rig was designed such that defects of varying sizes
could be seeded onto the outer race of a test bearing. The primary source of acoustic emission activity
from seeded defects was investigated, in addition to determining the relationship between defect sizes.
The second test program aimed to establish a correlation between acoustic emission activities with
increasing defect size. It was concluded that, the acoustic emission offers earlier fault detection and
improved identification capabilities than vibration analysis. Furthermore, the acoustic emission
technique also provided an indication of the defect size, allowing the user to monitor the rate of
degradation on the bearing. The acoustic emission acquisition system consisted of a piezoelectric type
sensor fitted onto the top half of the bearing housing. An increase in defect size resulted in an increase in
levels of acoustic emission energy for outer and inner race seeded defects. A correlation between the
geometric size of outer race defects and the acoustic emission bursts associated with such defects has
been shown, [48]. It was concluded that, the geometric defect size of outer race defects can be
determined from the acoustic emission waveform.
The effectiveness of acoustic emission technology in detecting and monitoring the initiation and
propagation of cracks was investigated, [49]. To undertake this task a special purpose test rig was built
that allowed for accelerated natural degradation of a bearing race. The results of classical Fast Fourier
Transformer of measured acoustic emission data were compared with three non-linear power spectral
estimation methods. It was concluded that sub-surface initiation and subsequent crack propagation can
be detected using a range of data analysis techniques on acoustic emission’s generated from natural
degrading bearings.
Cyclostationarity is a relatively new technique that offers diagnostic advantages for analysis of
vibrations from defective bearings. Similarly the Acoustic Emission technology has emerged as a viable
tool for preventive maintenance of rotating machines. The cyclic spectral correlation, a tool dedicated to
evidence the presence of Cyclostationarity, was compared with a traditional technique, the envelope
spectrum. The comparison showed that, the cyclic spectral correlation was most efficient for small
defect identification on outer race defects though the success was not mirrored on inner race defects. It
was concluded that, its offers better sensitivity to the continuous monitoring of defects compared to the
use of traditional temporal indicators, [50].
13
4. Temperature Measurements
The operating temperature plays a key role in the overall performance of a bearing system. Affected by
the bearing temperature are many critical parameters, such as the lubricant viscosity, load-carrying
capacity, load distribution and power loss. In contrast to the thermal analysis of journal bearings were
investigated. The influence of groove location and supply pressure on the performance of a steadily
loaded journal bearing with a single axial groove was studied, [51]. Hydrodynamic pressure and
temperature distributions on the bush surface, shaft temperature, flow rate, and bush torque were
measured at variable supply pressure, using bushes with a single groove located at three different
positions.
A new simple model was established for analyzing the thermally induced seizure of fully lubricated
eccentric circumferential groove journal bearings, [52]. The model was consisted of eccentric operation,
introduction of hydrodynamic lubricant flow rate component, and evaluation of friction power losses.
The decrease of viscosity, as a consequence of temperature increase, does not always limit clearance
loss, and the seizure process ends with a concentric journal bushing merged system. As a result, bearing
designers and users should check whether if several safe operation criteria were met. A three-
dimensional computational thermal contact model was developed. The frictional heat generated at the
contact interface is instantaneously partitioned between the bushing and the shaft. Two methods to
couple the heat and temperature at the contact interface were presented, [53]. Application of the model
to the heat transfer analysis of journal bearing systems experiencing oscillatory motion was presented.
Non-uniformly distributed frictional heat along the axial direction was considered. The model was
capable of predicting the transient temperature field for journal bearings. It can also be used to
determine the maximum contact temperature, which is difficult to be measured experimentally.
Applying the basic laws of heat transfer to rolling bearing assembly and using the lumped-mass
assumption, the steady- state temperature of the bearing components was estimated. The model
improved by solving a set of governing equations for the heat transfer in a complete assembly of a
tapered roller bearing including the housing, [54]. The model was able to estimate the temperature of the
bearing elements during the transient as well as the steady-state condition.
The thermal contact resistance between the ball and the inner and the outer rings of a space use deep
groove ball bearing was investigated assuming that heat transfer between smooth contacting elements
occurs through the elastic contact areas, [55]. The stationary bearing sustains axial, radial, or combined
loads under a steady state temperature condition was assumed. The shapes and sizes of the contact areas
were calculated using the Hertzian theory. In particular, the contact force for the axial load was
determined with careful consideration of the change in the contact angle induced by elastic deformation
at the contact area. The correlation between the experimental data and the calculated values confirms the
validity of the prediction method for the thermal contact resistance between the elements of a dry
bearing with a surface roughness under the mean temperature.
A new vacuum test rig designed to measure bearing conductance under simulated operational
conditions, [56]. Experimental variables include control of the bearing rotational speed, applied axial
load, and average bearing temperature and temperature gradient via an applied heat source/heat sink
mechanism. The experimental variables data was allowed parametric studies to be conducted under
14
controlled thermal and mechanical conditions, permitting the exploration of the influences of the
operational variables on bearing thermal conductance.
An analytical method to calculate the heat generation rate of supporting bearing in a kind of ball screw
system was studied, [57]. The operating conditions, rotation speed and external loads, were taken into
account for calculating heat generated by the bearings. The determination of bearing element torques
was mainly focused on both of the friction torque due to applied load and the sliding torque at the
contact area. Compared to the experiment results, the introduced method shows its validity with
accuracy calculation. The evolution of temperature with time in a deep groove ball bearing in an oil bath
lubrication system was studied. The model was proposed and simulations were carried out to investigate
the thermal behavior of ball bearings, [58]. The proposed model was used to predict the evolution of the
transient temperatures in the bearing components. Experiments were performed for different speeds and
loads to validate the model. The predicted temperatures under different loads and speeds were found to
be in close agreement with those measured experimentally.
5. Wear Debris Analysis
In the wear debris analysis, the presence of metallic particles in the lubricant is detecting using sensitive
sensors. Moreover, the spectrographic analysis of the different metallic elements in the lubricant can
facilitate the location of the fault [59]. The wear process of a given machine is usually the result of
several different, simultaneous wear mechanisms, each of which has its own way of affecting to the
machine’s operating environment and the changes that occur in it. If unfavorable operating conditions
persist, the wear and the dynamic forces might either cause parts of the machine to break or disturb the
machine’s operation. To allow detection at a sufficiently early stage and control of the wear process, the
amount, size, and appearance of wear debris particles in the machine’s lubricating oil must be monitored
[60].
6. Conclusions
In this paper, an attempt to summarize the recent research and developments in the detection techniques
for diagnosis and monitor the health of rolling element bearing faults has been made. Study of such
attribute defects gained importance due to increased awareness of cost of quality. From the previous
study, four different methods for detection and diagnosis of bearing defects namely: vibration
measurements, acoustic measurements, temperature measurements and wear debris analysis have been
identified. This study found that the vibration monitoring is the most useful technique because it is
reliable and very sensitive to fault severity. Also, it gives clear indications regarding the condition of the
bearing in question; in addition the level of vibrations and the frequency at which these vibrations occur
can serve in determining the exact location of the defect and possibly severity of such defect. It can be
concluded that the vibration analysis is most commonly accepted technique due to its ease of
application.
15
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