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 eigenvalue 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.
principal component analysis wavelet analysis signal processing
Zhengying Lin Weiyuan Shi Wei Zhang
School of Mechanical Engineering & Automation Fuzhou University Fuzhou,China School of Mechanical & Electrical Engineering Jinling Institute of Technology Nanjing,China School of Mechanical Engineering & Automation Fuzhou University Fuzhou, China
国际会议
长沙
英文
2470-2473
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）