Research on Signal Processing Method in Complex Textile Machinery System Based on Principal Component Analysis and Wavelet Analysis

This paper proposed a signal processing method based on principal component analysis (PCA) and wavelet analysis, aiming to reduce the dimension of the data and obtain both frequency and time localization information which could help to find abnormal phenomenon quickly and orient the position and the time of faults exactly in the complex textile machinery system. At first, the original signals were simplified by principal component transform, which was conducted by calculating the eigen value and eigenvector of correlation coefficient matrix, and by defining the first few PCs containing most of the variables according to contribute rate and cumulative contribute rate. Secondly, the restructured signals were decomposed into approximative and detailed ones for obtaining meaningful captures of instantaneous frequency by wavelet analysis. In this stage, Hilbert Envelope Analysis was also carried out to the first layer detail signal and to find its power spectrum. From practical application, this signal processing method was approved validated.