Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and Permutation Entropy

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (22) : 22-28.

ZHENG Xiaoxia 1 ZHOU Guowang 1 REN Haohan 2 FU Yang1

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Abstract

The incipient fault characteristic of rolling bearing vibration signal is weak and difficult to extract. In order to extract the characteristic parameters from the bearing vibration signal for bearing faults diagnosis, a signal characteristics extraction method based on variational mode decomposition and permutation entropy is proposed. The support vector machine is used for the fault recognition. Firstly, the bearing vibration signal is decomposed by the variational mode decomposition, and the intrinsic mode functions are obtained in different scales. Secondly, the permutation entropy of each intrinsic mode function is calculated and composed the multiscale feature vector. Finally, the high-dimensional feature vector is input to the support vector machine for the bearing fault diagnosis. The comparison is made with EEMD and WTD. The experimental results show that the proposed method can be effectively applied to diagnose rolling bearing faults.

Key words

variational mode decomposition / permutation entropy / support vector machine / rolling bearing / fault diagnosis

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ZHENG Xiaoxia 1 ZHOU Guowang 1 REN Haohan 2 FU Yang1 . Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and Permutation Entropy[J]. Journal of Vibration and Shock, 2017, 36(22): 22-28

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