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International Journal of Rotating Machinery
Volume 2017, Article ID 2607254, 11 pages
https://doi.org/10.1155/2017/2607254
Research Article

A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique

1Mechanical Engineering College, Shijiazhuang 050003, China
2Air Force Logistics College of PLA, Xuzhou 221000, China

Correspondence should be addressed to Hongru Li; moc.uhos@861rhil

Received 14 November 2016; Revised 28 December 2016; Accepted 9 January 2017; Published 24 January 2017

Academic Editor: Dong Wang

Copyright © 2017 Baiyan Chen et al. 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.

Abstract

The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance evaluation technique (DET) is proposed to solve this problem. Firstly, the vibration signal of the motor bearing is decomposed by ensemble empirical mode decomposition (EEMD). The proper intrinsic mode function (IMF) component that is the most sensitive to the degradation of the motor bearing is selected according to the sensitive IMF selection algorithm based on the similarity evaluation. Then the distance evaluation factor of each characteristic parameter is calculated by the DET method. The differential method is used to extract sensitive characteristic parameters which compose the characteristic matrix. And then the extracted degradation characteristic matrix is used as the input of support vector machine (SVM) to identify the degradation state. Finally, It is demonstrated that the proposed hybrid domain feature extraction method has higher recognition accuracy and shorter recognition time by comparative analysis. The positive performance of the method is verified.