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Entropy 2016, 18(3), 70; doi:10.3390/e18030070

Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings

1,†
,
1,* and 2,†
1
The School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333, Long Teng Road, Shanghai 201620, China
2
Mechanical Engineering Department, Virginia Tech MC-0238, 332 Randolph Hall, Blacksburg, VA 24060, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 18 December 2015 / Revised: 28 January 2016 / Accepted: 5 February 2016 / Published: 25 February 2016
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory)
View Full-Text   |   Download PDF [1838 KB, uploaded 25 February 2016]   |  

Abstract

A novel bearing vibration signal fault feature extraction and recognition method based on the improved local mean decomposition (LMD), permutation entropy (PE) and the optimized K-means clustering algorithm is put forward in this paper. The improved LMD is proposed based on the self-similarity of roller bearing vibration signal extending the right and left side of the original signal to suppress its edge effect. After decomposing the extended signal into a set of product functions (PFs), the PE is utilized to display the complexity of the PF component and extract the fault feature meanwhile. Then, the optimized K-means algorithm is used to cluster analysis as a new pattern recognition approach, which uses the probability density distribution (PDD) to identify the initial centroid selection and has the priority of recognition accuracy compared with the classic one. Finally, the experiment results show the proposed method is effectively to fault extraction and recognition for roller bearing. View Full-Text
Keywords: improved local mean decomposition; permutation entropy; optimizes K-means; fault extraction and recognition improved local mean decomposition; permutation entropy; optimizes K-means; fault extraction and recognition
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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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Shi, Z.; Song, W.; Taheri, S. Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings. Entropy 2016, 18, 70.

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