Author:
El Morsy Mohamed,Achtenova Gabriela
Abstract
The present article’s intent is to measure and identify the roller bearing inner race defect width and its corresponding characteristic frequency based on filtered time-domain vibration signal. In case localized fault occurs in a
bearing, the rolling elements encounter some slippage as the rolling elements enter and leave the bearing load
zone. As a consequence, the incidence of the impacts never reproduce exactly at the same position from one cycle
to another. Moreover, when the position of the defect is moving with respect to the load distribution zone of the
bearing, the series of impulses are modulated in amplitude in time-domain and the conforming Bearing Characteristic Frequencies (BCFs) arise in frequency domain. In order to verify the ability of time-domain in measuring
the fault of rolling bearing, an artificial fault is introduced in the vehicle gearbox bearing: an orthogonal placed
groove on the inner race with the initial width of 0.6mm approximately. The faulted bearing is a roller bearing
quantification of the characteristic features relevant to the inner race bearing defect. It is located on the gearbox
input shaft—on the clutch side. To jettison the frequency associated with interferential vibrations, the vibration
signal is filtered with a band-pass filter based on an optimal daughter Morlet wavelet function whose parameters
are optimized based on maximum Kurtosis (Kurt.). The residual signal is performed for the measurement of defect
width. The proposed technique is used to analyse the experimental signal of vehicle gearbox rolling bearing. The
experimental test stand is equipped with two dynamometer machines; the input dynamometer serves as an internal
combustion engine, the output dynamometer introduces the load on the flange of the output joint shaft. The Kurtosis and Pulse Indicator (PI) are selected as the evaluation parameters of the de-noising effect. The results show
the reliability of the proposed approach for identification and quantification of the characteristic features relevant
to the inner race bearing defect.
Publisher
International Institute of Acoustics and Vibration (IIAV)
Cited by
2 articles.
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1. Classification of Bearing Faults using Discrete Wavelet Packet Analysis and Support Vector Machine;2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT);2022-11-26
2. Fault Classification of Rolling Element Bearing in Machine Learning Domain;The International Journal of Acoustics and Vibration;2022-06-30