基于CM-SVDS-SVMD的滚动轴承故障特征提取方法

Research on rolling bearing fault feature extraction method based on CM-SVDS-SVMD

针对滚动轴承微弱故障特征信息易受噪声干扰提取困难的问题,提出一种新的滚动轴承故障特征提取方法,即协方差矩阵(covariance matrix,CM)、奇异值差分谱(singular value difference spectrum,SVDS)和奇异值中值分解(singular value median decomposition,SVMD)相结合。首先,考虑到旋转机械的故障特征,对轴承故障信号采用1步长方法构造Hankel矩阵;其次,考虑到信号的协方差矩阵对于信号自相关去噪的优势,进而计算Hankel的协方差矩阵并进行空间重构;再次,采用奇异值差分谱方法对重构后的协方差矩阵信号进行分解处理而实现初步降噪,通过奇异值中值分解方法对其进行分解和筛选处理而完成二次降噪,并根据处理后信号的频谱包络,实现轴承故障特征信息的提取;最后,通过滚动轴承仿真数据分析得出,所提方法能够有效提取出噪声信号的故障特征及其谐波,实现不同轴承故障类型特征的有效提取,为滚动轴承故障复杂信号处理和诊断提供了一种新的方法和途径。

A new method for extracting fault features of rolling bearings is proposed,which combines covariance matrix,singular value difference spectrum,and singular value median decomposition,to address the difficulty of extracting weak fault feature information from rolling bearings due to noise interference.Firstly,considering the fault characteristics of rotating machinery,a 1-step method is used to construct a Hankel matrix for bearing fault signals.Secondly,considering the advantage of the signal covariance matrix for signal autocorrelation denoising,the Hankel covariance matrix is calculated and phase space reconstruction is performed.Thirdly,the singular value difference spectrum method is used to decompose and process the reconstructed covariance matrix signal to achieve preliminary noise reduction.The singular value median decomposition method is used to decompose and filter it to complete secondary noise reduction.Based on the spectral envelope of the processed signal,bearing fault feature information is extracted.Finally,through the analysis of experimental data on rolling bearings,the results show that the proposed method can effectively extract the fault characteristics and harmonics of noise signals,achieve effective extraction of different types of bearing fault characteristics,and provide a new method and approach for complex signal processing and diagnosis of rolling bearing faults.