基于VMD-MPE结合概率神经网络和极限学习机的滚动轴承故障诊断分析

Fault Diagnosis Analysis of Rolling Bearing Based on VMD-MPE Combined with PNN and LEM

为揭示滚动轴承的多参量故障特性,提出了变分模态分解和多尺度排列熵相结合的方法进行特征提取并通过不同的算法进行故障诊断。首先对滚动轴承故障信号进行变分模态分解,其次利用多尺度排列熵量化各模态分量的故障特征,最后对计算所得熵值组成特征向量集将其导入概率神经网络、极限学习机和支持向量机中进行诊断,对比分析测试时间和正确概率。结果表明,该方法能有效提取故障特征并且准确实现故障模式的分类识别,进而提高了故障识别概率。

In order to reveal the multi-parameter fault characteristics of rolling bearings, a method combining variational mode decomposition and multi-scale permutation entropy was proposed for feature extraction and fault diagnosis by different algorithms.The rolling bearing fault signal was decomposed by variational modes. Secondly, the fault characteristics of each modal component were quantified by multi-scale permutation entropy, and finally, the calculated entropy was composed of feature vector set, which was introduced into probabilistic neural network, limit learning machine and support vector machine for diagnosis, and the test time and correct probability were compared and analyzed. The results show that this method can extract fault features effectively and realize the classification and recognition of fault modes accurately, the probability of fault identification improves.