Abstract: The Rolling Element Bearing (REB) vibration diagnosis is worth of special interest by the variety of REB and the wide necessity of those elements in industrial applications. The presence of a localized fault in a REB gives rise to a vibrational response, characterized by the modulation of a carrier signal. Frequency content of carrier signal (Spectral Frequency –f) is mainly related to resonance frequencies of the REB. This carrier signal is modulated by another signal, governed by the periodicity of the fault impact (Cyclic Frequency –α). In this sense, REB fault vibration response gives rise to a second-order cyclostationary signal. Second order cyclostationary signals could be represented in a bi-spectral map, where Spectral Coherence –SCoh are plotted against f and α. The Improved Envelope Spectrum –IES, is a useful approach to execute REB fault diagnosis. IES could be applied by the integration of SCoh over a predefined bandwidth on the f axis. Approaches to select f-bandwidth have been recently exposed by the definition of a metric which intends to evaluate the magnitude of the IES at the fault characteristics frequencies. This metric is represented in a 1/3-binary tree as a function of the frequency bandwidth and centre. Based on this binary tree the optimal frequency band is selected. However, some advantages have been seen if the metric is changed, which in fact tends to dictate different optimal f-bandwidth and so improve the IES representation. This paper evaluates the behaviour of the IES from a different metric optimization. This metric is based on the sample correlation coefficient, detecting high peaks in the selected frequencies while penalizing high peaks in the neighbours of the selected frequencies. Prior results indicate an improvement on the signal-noise ratio (SNR) on around 86% of samples analysed, which belong to IMS database.
Abstract: Maintenance and design engineers have great concern
for the functioning of rotating machineries due to the vibration
phenomenon. Improper functioning in rotating machinery originates
from the damage to rolling element bearings. The status of rolling
element bearings require advanced technologies to monitor their
health status efficiently and effectively. Avoiding vibration during
machine running conditions is a complicated process. Vibration
simulation should be carried out using suitable sensors/ transducers to
recognize the level of damage on bearing during machine operating
conditions. Various issues arising in rotating systems are interlinked
with bearing faults. This paper presents an approach for fault
diagnosis of bearings using neural networks and time/frequencydomain
vibration analysis.
Abstract: In rotating machinery one of the critical components
that is prone to premature failure is the rolling bearing.
Consequently, early warning of an imminent bearing failure is much
critical to the safety and reliability of any high speed rotating
machines. This study is concerned with the application of Recurrence
Quantification Analysis (RQA) in fault detection of rolling element
bearings in rotating machinery. Based on the results from this study it
is reported that the RQA variable, percent determinism, is sensitive
to the type of fault investigated and therefore can provide useful
information on bearing damage in rolling element bearings.
Abstract: Rolling element bearings are widely used in industry,
especially where high load capacity is required. The diagnosis of
their conditions is essential matter for downtime reduction and saving
cost of maintenance. Therefore, an intensive analysis of frequency
spectrum of their faults must be carried out in order to determine the
main reason of the fault. This paper focus on a beating phenomena
observed in the waveform (time domain) of a cylindrical rolling
element bearing. The beating frequencies were not related to any
sources nearby the machine nor any other malfunctions (unbalance,
misalignment ...etc). More investigation on the spike energy and the
frequency spectrum indicated a problem with races of the bearing.
Multi-harmonics of the fundamental defects frequencies were
observed. Two of them were close to each other in magnitude those
were the source of the beating phenomena.
Abstract: Effectiveness of Artificial Neural Networks (ANN)
and Support Vector Machines (SVM) classifiers for fault diagnosis of
rolling element bearings are presented in this paper. The
characteristic features of vibration signals of rotating driveline that
was run in its normal condition and with faults introduced were used
as input to ANN and SVM classifiers. Simple statistical features such
as standard deviation, skewness, kurtosis etc. of the time-domain
vibration signal segments along with peaks of the signal and peak of
power spectral density (PSD) are used as features to input the ANN
and SVM classifier. The effect of preprocessing of the vibration
signal by Discreet Wavelet Transform (DWT) prior to feature
extraction is also studied. It is shown from the experimental results
that the performance of SVM classifier in identification of bearing
condition is better then ANN and pre-processing of vibration signal
by DWT enhances the effectiveness of both ANN and SVM classifier
Abstract: The vibrations produced by a single point defect on
various parts of the bearing under constant radial load are predicted
by using a theoretical model. The model includes variation in the
response due to the effect of bearing dimensions, rotating frequency
distribution of load. The excitation forces are generated when the
defects on the races strike to rolling elements. In case of the outer
ring defect, the pulses generated are with periodicity of outer ring
defect frequency where as for inner ring defect, the pulses are with
periodicity of inner ring defect frequency. The effort has been carried
out in preparing the physical model of the system. Different defect
frequencies are obtained and are used to find out the amplitudes of
the vibration due to excitation of the bearing parts. Increase in the
radial load or severity of the defect produces a significant change in
bearing signature characteristics.
Abstract: The effect of the rotational speed and axial torque on
the diagnostics of tapered rolling element bearing defects was
investigated. The accelerometer was mounted on the bearing housing
and connected to Sound and Vibration Analyzer (SVAN 958) and
was used to measure the accelerations from the bearing housing. The
data obtained from the bearing was processed to detect damage of the
bearing using statistical tools and the results were subsequently
analyzed to see if bearing damage had been captured. From this study
it can be seen that damage is more evident when the bearing is
loaded. Also, at the incipient stage of damage the crest factor and
kurtosis values are high but as time progresses the crest factors and
kurtosis values decrease whereas the peak and RMS values are low at
the incipient stage but increase with damage.