相关奇异值比的SVD在轴承故障诊断中的应用

基于Hankel矩阵的奇异值分解(Singular value decomposition,SVD)方法在信号处理、故障诊断领域得到了广泛应用.其降噪性能受选取的重构分量、Hankel矩阵结构、分析的数据点数的影响,对此进行了系统的研究,提出了基于相关奇异值比的SVD(Correlated singular value...

Application of SVD Based on Correlated Singular Value Ratio in Bearing Fault Diagnosis

The SVD method based on Hankel matrix is widely used in signal processing and fault diagnosis. Its noise reduction performance is affected by the selected reconstruction component, the structure of the Hankel matrix, and the number of points of the analysis data. Based on this, a systematic research is carried out, and the SVD based on correlated singular value ratio (C-SVR SVD) is proposed, and successfully applied to bearing fault diagnosis. First, for the problem of determining the reconstruction components of SVD, a method combining singular value ratio (SVR) and cross-correlation coefficient is proposed; secondly, the structure of Hankel matrix is studied, and a structure optimization method based on SVR and kurtosis indicator is proposed. The structure optimization method of the indicator. Then, the number of analyzed data points was analyzed and discussed, and constraints were given. Finally, the C-SVR SVD method is applied to the analysis of the bearing fault simulation signal and the actual bearing fault signal, which verifies the effectiveness and superiority of the C-SVR SVD method.