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Indexed by:期刊论文

Date of Publication:2010-07-01

Journal:MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Included Journals:SCIE、EI

Volume:24

Issue:5,SI

Page Number:1458-1472

ISSN No.:0888-3270

Key Words:Rolling element bearing; Fault diagnosis; Morlet wavelet; Genetic algorithm; Autocorrelation enhancement

Abstract:The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is very simple for operator to identify the bearing fault type. Moreover, the proposed method can be conducted in an almost automatic way. The results obtained from simulated and practical experiments prove that the proposed method is very effective for bearing faults diagnosis. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.