Classification of rolling element bearing fault using singular value
Journal of Quality in Maintenance Engineering
ISSN: 1355-2511
Article publication date: 23 September 2019
Issue publication date: 23 March 2020
Abstract
Purpose
The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig.
Design/methodology/approach
An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal.
Findings
Singular values of the statistical features can be effectively used for REB classification.
Practical implications
REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines.
Originality/value
It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.
Keywords
Acknowledgements
The authors express their gratitude to Mrs. Mamatha H., Lecturer in Mathematics, Govt. P U College Mulky, India, for her help in applying the SVD concept for dimensionality reduction used in the development of health index.
Citation
Kumar, H.S., Pai, P.S. and S, S.N. (2020), "Classification of rolling element bearing fault using singular value", Journal of Quality in Maintenance Engineering, Vol. 26 No. 2, pp. 181-197. https://doi.org/10.1108/JQME-12-2016-0083
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited