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Entropy 2017, 19(4), 176; doi:10.3390/e19040176

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

1
Joint Research Lab of Intelligent Perception and Control, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Long Teng Road, Shanghai 201620, China
2
Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy
3
School of Information Science and Technology, East China Normal University, Shanghai 200241, China
4
Ocean College, Zhejiang University, Hangzhou 316021, China
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 8 January 2017 / Revised: 3 March 2017 / Accepted: 14 April 2017 / Published: 19 April 2017
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory II)
View Full-Text   |   Download PDF [2881 KB, uploaded 19 April 2017]   |  

Abstract

Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing. View Full-Text
Keywords: improved LMD; multi-scale permutation entropy; MI; FNN; delay time; embedding dimension; HMM; back-propagation (BP); bearing fault diagnosis improved LMD; multi-scale permutation entropy; MI; FNN; delay time; embedding dimension; HMM; back-propagation (BP); bearing fault diagnosis
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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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Gao, Y.; Villecco, F.; Li, M.; Song, W. Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis. Entropy 2017, 19, 176.

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