Classification of rolling element bearing fault using singular value

• Published in: Journal of quality in maintenance engineering Vol. 26; no. 2; pp. 181 - 197
• Main Authors: Kumar, H.S, Pai, P. Srinivasa, S, Sriram N
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 11.05.2020; Emerald Group Publishing Limited
• Subjects: Bearings; Classification; Critical components; Customization; Decomposition; Discrete Wavelet Transform; Discriminant analysis; Fault diagnosis; Feature extraction; Fourier transforms; Kurtosis; Literature reviews; Methods; Noise reduction; Point defects; Principal components analysis; Roller bearings; Rotary machines; Signal processing; Soft computing; Support vector machines; Vibration; Wavelet transforms; Feature extraction; Wavelet transform
• ISSN: 1355-2511; 1758-7832
• DOI: 10.1108/JQME-12-2016-0083

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.