The Fault Detection of Multi-Sensor Based on Multi-Scale PCA

摘要：

　　Multi-Scale Principal Component Analysis (MSPCA) for sensor fault detection is discussed to resolve the problem that the traditional MSPCA cant realize the comprehensive sensor fault detection.MSPCA combines the decorrelation ability of PCA for the linear variables with the ability of wavelet analysis to extract deterministic features and approximately decomposition correlation of variable.MSPCA computes wavelet coefficients of the PCA at each scale and then combines the results at relevant scales.Due to its multi-scale properties,MSPCA is appropriate for the data modeling along with the time and frequency changes.The superior performance of MSPCA for process fault monitoring is illustrated by simulation results.

关键词： Principal Component Analysis Fault Detection Multi-Scale

作者: Zhanfeng Wang Hailian Du Feng Lv Wenxia Du

作者单位: The Department of information engineering, Shijiazhuang University of Economics, Shijiazhuang, 05003 College of Career Technology, Hebei Normal University, Shijiazhuang 050023

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 4697-4700

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）