基于PPCA-EWT的滚动轴承轻微故障诊断

针对经验小波变换(empirical wavelet transform,简称EWT)在强背景噪声下对轴承的轻微故障特征提取不足的问题,提出了概率主成分分析(probabilistic principal component analysis,简称PPCA)结合EWT的滚动轴承轻微故障诊断方法。首先,对信号做PPCA预处理,提取信号主要故障特征成分,去除强背景噪声干扰;然后,采用EWT方法分解轴承故障信号,按相关系数-峭度准则选出故障特征较为明显的分量,并将所选分量重构故障信号;最后,对信号采取包络分析,提取出轴承故障特征。仿真和实验结果表明,该方法能够有效地诊断出轴承故障且效果优于对信号进行EWT包络分析。

Rolling Bearing Weak Fault Diagnosis Based on PPCA and EWT

It is difficult for the empirical wavelet transform (EWT) to extract the fault feature of bearing weak fault in the strong noisy environment. In the light of this problem, a new rolling bearing weak fault diagnosis method based on the probabilistic principal component analysis (PPCA) and EWT is proposed. First, the raw signal is analyzed using PPCA to extract fault feature and restrain noise interference. Second, the signal is decomposed using EWT. The most qualified components are selected to reconstruct signals using the correlation coefficient-kurtosis criteria. Finally, the envelope spectrum is performed to extract fault features of the rolling bearing signals. The simulation and experimental data are analyzed using the proposed PPCA-EWT and EWT-based envelope analysis. The results show that the noise is eliminated and the fault feature is enhanced by the PPCA-EWT process. is the method is effective in the rolling bearing weak fault detection.