Application of correlation matching for automatic bearing fault diagnosis

Liu, Xiaofeng, Bo, Lin, He, Xingxi and Veidt, Martin (2012) Application of correlation matching for automatic bearing fault diagnosis. Journal of Sound and Vibration, 331 26: 5838-5852. doi:10.1016/j.jsv.2012.07.022

Author Liu, Xiaofeng
Bo, Lin
He, Xingxi
Veidt, Martin
Title Application of correlation matching for automatic bearing fault diagnosis
Journal name Journal of Sound and Vibration   Check publisher's open access policy
ISSN 0022-460X
Publication date 2012-12-01
Sub-type Article (original research)
DOI 10.1016/j.jsv.2012.07.022
Volume 331
Issue 26
Start page 5838
End page 5852
Total pages 15
Place of publication London, United Kingdom
Publisher Elsevier
Language eng
Abstract This paper introduces an automatic feature extraction algorithm for bearing fault diagnosis using correlation filtering-based matching pursuit. This algorithm is described and investigated in theory and practice on both simulated and real bearing vibration signals. First, the vibration model for rolling bearing with fault is derived. Then, the numerical simulation signal being taken as an example, the principle of matching pursuit is mathematically explained and its drawbacks are analyzed. Afterward, to enhance the similarity of model related to the bearing faulty impulses, the model shape parameters are optimized using spectrum kurtosis and smoothing index. After that, the model with optimum shape and period parameters is taken as a template to approximate the impulses in faulty bearing signal. Finally, based on maximizing correlation principle, the optimized cycle parameter being as impuls e repetition period is matched up. The proposed method has been successfully applied in actual vibration signals of rolling element bearing with different faults.
Keyword Rolling element bearing
Bearing fault diagnosis
Extraction algorithm
Matching pursuit
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online: 29 August 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Engineering Publications
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