NDLI: Extraction of failure characteristics of rolling element bearing based on wavelet transform under strong noise

Content Provider	IEEE Xplore Digital Library
Author	Zhang Hui Wang Shu-juan Zhai Guo-fia
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. Eng., Harbin Inst. of Technol., China (Zhang Hui; Wang Shu-juan; Zhai Guo-fia)
Abstract	There have been a lot of researches on diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under strong noise. Therefore, this paper proposes auto-correlation, cross-correlation and weighted average fault diagnosis methods based on wavelet transform (WT) de-noising which combine correlation analysis with WT for the first time. These three methods compute the auto-correlation, the cross-correlation and the weighted average of the measured vibration signals, then de-noise by thresholding and compute the auto-correlation of de-noised coefficients of WT and FFT of energy sequence. The simulation results indicate that all methods enhance the capabilities of fault diagnosis of rolling bearing and pick up the fault characteristics effectively.
Starting Page	713
Ending Page	717
File Size	675025
Page Count	5
File Format	PDF
ISBN	0780386620
DOI	10.1109/ICIT.2004.1490162
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-08
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rolling bearings Wavelet transforms Autocorrelation Wavelet analysis Fault diagnosis Vibration measurement Signal analysis Noise reduction Energy measurement Computational modeling
Content Type	Text
Resource Type	Article

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