Rolling element bearing fault diagnostics: Development of health index

This article develops and compares health indices using different approaches namely singular value decomposition, average value of the cumulative feature and Mahalanobis distance for assessing the rolling element bearing condition. The vibration signals for four conditions of rolling element bearing...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2017-11, Vol.231 (21), p.3923-3939
Main Authors: Kumar, HS, Pai, Srinivasa P, Sriram, NS, Vijay, GS
Format: Article
Language:English
Subjects:
Bearing
Discriminant analysis
Fault diagnosis
Feature extraction
Health
Noise reduction
Singular value decomposition
Wavelet analysis
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Summary:This article develops and compares health indices using different approaches namely singular value decomposition, average value of the cumulative feature and Mahalanobis distance for assessing the rolling element bearing condition. The vibration signals for four conditions of rolling element bearing are acquired from a customized bearing test rig under variable load conditions. Seventeen statistical features are extracted from wavelet coefficients of the denoised signals. Feature selection is performed using singular value decomposition and kernel Fisher discriminant analysis. These selected features are used in these three approaches to develop health indices. Finally, a comparison of the three proposed approaches is made to select the best approach which can be effectively used for fault diagnosis of rolling element bearings.
ISSN:0954-4062
2041-2983
DOI:10.1177/0954406216656214