基于变分模态分解和SVM的滚动轴承故障诊断

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (18) : 252-256.

论文

王新，闫文源

作者信息 +

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

WANG Xin，YAN Wen-yuan

Author information +

文章历史 +

摘要

针对滚动轴承振动信号的非平稳特征和现实中难以获得大量故障样本的实际情况，提出了基于变分模态分解（Variational mode decomposition，VMD）与支持向量机（Support vector machine，SVM）相结合的滚动轴承故障诊断方法。该方法融合了变分模态分解和支持向量机的优势，通过变分模态分解将滚动轴承振动信号分解成若干个本征模态函数分量，轴承发生不同故障时，不同本征模态函数内的频带能量会发生变化，从包含有主要故障信息的模态分量中提取能量特征作为SVM的输入，判断轴承的工作状态和故障类型。实验结果表明，该方法在少量样本情况下仍能有效地对轴承的工作状态和故障类型进行分类。

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.

导出引用

王新，闫文源. 基于变分模态分解和SVM的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(18): 252-256

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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