Title: Non-stationary signal processing for bearing health monitoring

Authors: R.X. Gao, R. Yan

Addresses: Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, USA. ' Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, USA

Abstract: Signals generated by transient vibrations in rolling bearings due to structural defects are non-stationary in nature, and reflect upon the operation condition of the bearing. Consequently, effective processing of non-stationary signals is critical to bearing health monitoring. This paper presents a comparative study of four representative time-frequency analysis techniques commonly employed for non-stationary signal processing. The analytical framework of the short-time Fourier transform, wavelet transform, wavelet packet transform, and Hilbert-Huang transform are first presented. The effectiveness of each technique in detecting transient features from a time-varying signal is then examined, using an analytically formulated test signal. Subsequently, the performance of each technique is experimentally evaluated, using realistic vibration signals measured from a bearing test system. The results demonstrate that selecting appropriate signal processing technique can significantly affect defect identification and consequently, improve the reliability of bearing health monitoring.

Keywords: bearing health monitoring; non-stationary signals; time-frequency analysis; signal processing; transient vibrations; rolling bearings; structural defects; defect identification; reliability; sensors; fault diagnosis; intelligent manufacturing; online monitoring.

DOI: 10.1504/IJMR.2006.010701

International Journal of Manufacturing Research, 2006 Vol.1 No.1, pp.18 - 40

Published online: 19 Aug 2006 *

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