基于迭代滤波和最大相关峭度解卷积的滚动轴承故障诊断方法

Roller Bearing Fault Diagnosis Method Based on Iterative Filtering and Maximum Correlation Kurtosis Deconvolution

针对噪声环境下滚动轴承故障特征提取的难题,提出了基于迭代滤波和最大相关峭度解卷积的滚动轴承故障诊断方法。首先对轴承振动信号进行迭代滤波分解,然后通过相关系数和峭度准则筛选出敏感的内禀模态分量,对敏感的内禀模态分量进行最大相关峭度解卷积降噪,最后对降噪的信号进行频谱分析完成轴承故障诊断。对轴承仿真信号和滚动轴承故障振动试验信号进行了分析,结果表明文中方法能有效地应用于滚动轴承故障诊断。

In order to solve the problem of fault feature extraction of rolling bearing in noisy environment, a rolling bearing fault diagnosis method based on iterative filtering and maximum correlation kurtosis deconvolution was proposed. Firstly, the vibration signal of the bearing was filtered and iteratively decomposed using iterative filtering. Then the sensitive intrinsic modal components were selected through the correlation coefficient and kurtosis criteria, and the sensitive intrinsic modal components were de-noised with the maximum correlation kurtosis deconvolution. Finally, the spectrum analysis of the noise reduction signal was used to diagnose the bearing fault. The bearing simulation signal and rolling bearing fault vibration test signal are analyzed and the results show that this method can be effectively applied to rolling bearing fault diagnosis.