Detecting faults early on is important in order to maintain the health
of rotating machinery. Incipient faults in rolling-element bearings
lead to the generation of micro-defects which create weak transient
impact signals. However, these are often buried within the
background vibration of the machine and structural transmission
effects. Indicators based on traditional time-domain methods
possess sensitivity to impulsive damage, but when the signal
exhibits non-stationarity or a noisy trend. The current study
compares time-domain statistical parameters and FFT spectral
analysis for progressive inner race damage in an NJ307 cylindrical
roller bearing. A wire electrical-discharge-machining technique
produces artificial inner-race defects with sizes of 0.25 mm, 0.50
mm, 0.75 mm and 1.00 mm, while a healthy bearing serves as the
reference condition. Vibration is measured with a single-axis
accelerometer connected to an eight-channel Dewesoft data-
acquisition system with a sampling frequency of 20 kHz; the shaft
speed is 800 rpm by use of a variable-frequency drive.
The RMS, standard deviation, peak value, crest factor,
kurtosis, and form factor are taken into account during the analysis.
FFT spectra are observed at the calculated inner-race fault
frequency and its harmonics. The findings reveal that at the earliest
damage stage, there is only a gradual change in both RMS and
standard deviation. In contrast, changes in peak value, crest factor,
kurtosis, and amplitudes of the BPFI-related FFT are more robust indicators of progressive damage. The FFT
provides statistically interpretable frequency information, which scalar time-domain features cannot. As
suggested by the findings, the proposition is for a combined diagnostic procedure in which the time-domain
features provide a rapid screening while the FFT confirms the bearing-fault frequency.
Keywords: cylindrical roller bearing; incipient fault; dynamic response; condition monitoring
Umakant Banswarti, S. Pandey· International Journal of Cre...· 0 citations
Rolling element bearings are used in rotating machines in aviation,
chemical, and nuclear industries. A failure to detect faults in the
rolling bearing causes unexpected breakdown of rotating machines.
Detecting bearing defects early on is still a challenge since micro-
faults have less energy. Early-stage defects from fatigue,
misalignment, overload and poor lubrication create low-energy
signals that can be masked. This article summarises some key
measurement methods, i.e. vibration and acoustic signal
measurement, tribological parameter analysis, wear debris analysis,
and thermal measurement, in rolling element bearings. All the
methods will be evaluated with respect to a sensing mechanism,
signal processing algorithm, micro-defect sensitivity, and operational
constraints of the anti-friction bearing. To deal with the drawbacks of
single sensors, we demonstrate the use of recent advancements in
multi-sensor data fusion and machine learning models through
examples where kinetic, acoustic and chemical metrics are fused for
improved classification outcomes. In summary, multi-sensor data
fusion greatly improves the accuracy of early fault detection, and it
can greatly enhance predictive maintenance systems.
Keywords: Vibration, Acoustic, Wear Debris, Tribology, Condition
Monitoring, Bearing
Umakant Banswarti, S. Pandey· International Journal of Cre...· 0 citations
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