滚动轴承早期故障信号特征微弱且难以提取，为了从轴承振动信号中提取特征参数用于轴承故障诊断和识别，提出基于变分模态分解(Variational Mode Decomposition,VMD)和排列熵(Permutation Entropy, PE)的信号特征提取方法，并采用支持向量机(Support Vector Machine,SVM)进行故障识别。首先对轴承振动信号进行变分模态分解，得到不同尺度的本征模态函数；然后，计算各本征模态函数的排列熵，组成多尺度的复杂性度量特征向量；最后，将高维特征向量输入基于支持向量基建立的分类器进行故障识别分类。通过滚动轴承实验数据分析了算法中参数选取问题，将该方法应用于滚动轴承实验数据，并与集合经验模态分解和小波包分解进行对比，分析结果表明，基于变分模态分解和排列熵的诊断方法有更高的诊断准确率，能够有效实现滚动轴承的故障诊断。
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.
关键词
变分模态分解 /
排列熵 /
支持向量机 /
滚动轴承 /
故障诊断
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Key words
variational mode decomposition /
permutation entropy /
support vector machine /
rolling bearing /
fault diagnosis
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参考文献
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