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Wind Power Gear Box Fault Diagnosis Based on DEMD Local Frequency Entropy and SVM |
MENG Zong1,2,LIU Dong1,YUE Jian-hui1,ZHAN Xu-yang1,MA Zhao1,LI Jing1 |
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Abstract In order to extract the constituent of useful information from the nonlinear and non-stationary wind gearbox fault signal. A new approach for wind power gear box fault diagnosis based on the combination of differential-based empirical mode decomposition(DEMD), local frequency entropy and support vector machine(SVM) is proposed. Firstly, fault vibration signal is filtered with adaptive multi-scale mathematical morphology. Then mechanical vibration signal is decomposed with DEMD to obtain a certain number of intrinsic mode functions(IMF). Then the local time-frequency entropy of the IMF components are calculated and used as the eigenvectors of SVM. Finally, the eigenvectors are put into SVM to identify the state of the wind power gear box. The experimental results show that the method based on the combination of DEMD, local time-frequency entropy and SVM can be used to recognize and classify rolling bearing fault signals accurately and effectively.
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Received: 03 August 2015
Published: 16 June 2017
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Corresponding Authors:
Zong MENG
E-mail: mzysu@ysu.edu.cn
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