Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact fault diagnosis

Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing... The performance of sparse features extraction by commonly used K-Singular Value Decomposition (K-SVD) method depends largely on the signal segment selected in rolling bearing diagnosis, furthermore, the calculating speed is relatively slow and the dictionary becomes so redundant when the fault signal is relatively long. A new sliding window denoising K-SVD (SWD-KSVD) method is proposed, which uses only one small segment of time domain signal containing impacts to perform sliding window dictionary learning and select an optimal pattern with oscillating information of the rolling bearing fault according to a maximum variance principle. An inner product operation between the optimal pattern and the whole fault signal is performed to enhance the characteristic of the impacts' occurrence moments. Lastly, the signal is reconstructed at peak points of the inner product to realize the extraction of the rolling bearing fault features. Both simulation and experiments verify that the method could extract the fault features effectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Sound and Vibration Elsevier

Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact fault diagnosis

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0022-460X
eISSN
1095-8568
D.O.I.
10.1016/j.jsv.2018.01.051
Publisher site
See Article on Publisher Site

Abstract

The performance of sparse features extraction by commonly used K-Singular Value Decomposition (K-SVD) method depends largely on the signal segment selected in rolling bearing diagnosis, furthermore, the calculating speed is relatively slow and the dictionary becomes so redundant when the fault signal is relatively long. A new sliding window denoising K-SVD (SWD-KSVD) method is proposed, which uses only one small segment of time domain signal containing impacts to perform sliding window dictionary learning and select an optimal pattern with oscillating information of the rolling bearing fault according to a maximum variance principle. An inner product operation between the optimal pattern and the whole fault signal is performed to enhance the characteristic of the impacts' occurrence moments. Lastly, the signal is reconstructed at peak points of the inner product to realize the extraction of the rolling bearing fault features. Both simulation and experiments verify that the method could extract the fault features effectively.

Journal

Journal of Sound and VibrationElsevier

Published: May 12, 2018

References

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