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Appl. Sci. 2016, 6(5), 154; doi:10.3390/app6050154

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

1,2,3
,
1,2,3,* , 1,2,3
and
1,2,3
1
College of Information Engineering, Capital Normal University, Beijing 100048, China
2
Beijing Key Laboratory of Light Industrial Robot and Safety Verification, Capital Normal University, Beijing 100048, China
3
Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Academic Editor: Hung-Yu Wang
Received: 11 March 2016 / Revised: 10 May 2016 / Accepted: 11 May 2016 / Published: 18 May 2016
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Abstract

Singular value decomposition (SVD) is a widely used and powerful tool for signal extraction under noise. Noise attenuation relies on the selection of the effective singular value because these values are significant features of the useful signal. Traditional methods of selecting effective singular values (or selecting the useful components to rebuild the faulty signal) consist of seeking the maximum peak of the differential spectrum of singular values. However, owing to the small number of selected effective singular values, these methods lead to excessive de-noised effects. In order to get a more appropriate number of effective singular values, which preserves the components of the original signal as much as possible, this paper used a difference curvature spectrum of incremental singular entropy to determine the number of effective singular values. Then the position was found where the difference of two peaks in the spectrum declines in an infinitely large degree for the first time, and this position was regarded as the boundary of singular values between noise and a useful signal. The experimental results showed that the modified methods could accurately extract the non-stationary bearing faulty signal under real background noise. View Full-Text
Keywords: singular value decomposition; incremental spectrum of singular entropy; curvature spectrum; noise-reduction; adjacent maximum peaks singular value decomposition; incremental spectrum of singular entropy; curvature spectrum; noise-reduction; adjacent maximum peaks
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gao, J.; Wu, L.; Wang, H.; Guan, Y. Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising. Appl. Sci. 2016, 6, 154.

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