An Extended Wavelet Spectrum for Bearing Fault Diagnostics

被引：55

作者：

Liu, Jie ^{[1
]}

Wang, Wilson ^{[2
]}

Golnaraghi, Farid ^{[1
]}

机构：

[1] Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada

[2] Lakehead Univ, Dept Mech Engn, Thunder Bay, ON P7B 5E1, Canada

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2008年 / 57卷 / 12期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Autocorrelation spectrum; bearing fault diagnostics; extended wavelet spectrum (EWS) analysis; shaft speed detection;

D O I：

10.1109/TIM.2008.927211

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Rolling-element bearings are widely used in various mechanical and electrical systems. A reliable online bearing fault-diagnostic technique is critically needed to prevent the system's performance degradation and malfunction. In this paper, an extended wavelet spectrum analysis technique is proposed for a more positive assessment of bearing health conditions. Two strategies have been suggested for different wavelet function implementation. Two statistical indexes are proposed to quantify the resulting wavelet (coefficient) functions. Based on the information provided by these indexes, the wavelet functions can be deployed more effectively over the designated frequency bands. An extended Shannon function is proposed to synthesize the wavelet coefficients over selected bandwidths to enhance feature characteristics. An averaged autocorrelation power spectrum is adopted to highlight bearing characteristics. The viability of the developed technique is verified by online experimental tests corresponding to different bearing conditions.

引用

页码：2801 / 2812

页数：12

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