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Shock and Vibration - Volume 2015 2015, Article ID 893504, 9 pages -

Research Article

School of Mechatronics and Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China

The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China

Chongqing Academy of Metrology and Quality Inspection, Chongqing 401123, China

Chongqing University of Education, Chongqing 400065, China

Received 4 October 2014; Revised 20 November 2014; Accepted 28 November 2014

Academic Editor: Alicia Gonzalez-Buelga

Copyright © 2015 Shaojiang Dong 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.


In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm PSO and the nonlinear manifold learning algorithm local tangent space alignment LTSA is proposed. Firstly, the signal is purified by the morphological filter; the filter’s structure element SE is selected by PSO method. Then the filtered signals are decomposed by the empirical mode decomposition EMD method, and the extract features are mapped into the LTSA to extract the character features; then the support vector machine SVM model is used to achieve the rotating machine fault diagnosis. The proposed method is evaluated by vibration signals measured from bearings with faults. Results show that the method can effectively remove the noise and extract the fault features, so the rotating machine fault diagnosis can be achieved effectively.

Autor: Shaojiang Dong, Lili Chen, Baoping Tang, Xiangyang Xu, Zhengyuan Gao, and Juan Liu



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