Fault diagnosis of roller bearings based on variational mode decomposition and SVM

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (18) : 252-256.

WANG Xin，YAN Wen-yuan

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Abstract

Aiming at the non-stationary features of vibration signals of the roller bearing and the difficulty to obtain a large number of fault samples in practice, a fault diagnosis method based on the variational mode decomposition and the support vector machine (SVM) was put forward. This method combines the advantages of the variational mode decomposition and the SVM. Original bearing acceleration vibration signals are decomposed into a finite number of intrinsic mode functions. The frequency band energy of different intrinsic mode functions changes when the fault occurs. To identify the fault pattern and the condition, the frequency band energy features extracted from a number of intrinsic mode functions containing the most dominant fault information can serve as input vectors of the SVM. Practical examples show that the proposed method can classify the working condition of the bearing accurately and effectively even in the case of smaller number of samples.

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variational mode decomposition / support vector machine / roller bearing / fault diagnosis

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WANG Xin，YAN Wen-yuan. Fault diagnosis of roller bearings based on variational mode decomposition and SVM[J]. Journal of Vibration and Shock, 2017, 36(18): 252-256

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