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Entropy 2017, 19(1), 14; doi:10.3390/e19010014

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

1,2,3,4,5
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1,2,3,4,5,* and 1
1
Software Institute, Dalian Jiaotong University, Dalian 116028, China
2
Sichuan Provincial Key Lab of Process Equipment and Control, Sichuan University of Science and Engineering, Zigong 64300, China
3
Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, China
4
The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
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Dalian Key Laboratory of Welded Structures and Its Intelligent Manufacturing Technology (IMT) of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China
*
Author to whom correspondence should be addressed.
Academic Editors: Carlo Cattani and Kevin H. Knuth
Received: 23 September 2016 / Revised: 3 December 2016 / Accepted: 26 December 2016 / Published: 31 December 2016
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory II)
View Full-Text   |   Download PDF [3905 KB, uploaded 31 December 2016]   |  

Abstract

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery. View Full-Text
Keywords: feature extraction; motor bearing; ensemble empirical mode decomposition (EEMD); multi-scale fuzzy entropy; correlation coefficient method; SVM; fault diagnosis feature extraction; motor bearing; ensemble empirical mode decomposition (EEMD); multi-scale fuzzy entropy; correlation coefficient method; SVM; fault diagnosis
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Zhao, H.; Sun, M.; Deng, W.; Yang, X. A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing. Entropy 2017, 19, 14.

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