会议专题

Sensor Fault Detection and Identification using Kernel PCA and Its Fast Data Reconstruction

In this paper, a novel sensor fault detection and identification technique based on kernel principal component analysis (KPCA) and its fast data reconstruction is presented. Although it has been proved that KPCA shows a better performance for sensor fault detection, the fault identification method has rarely been developed. Using the fast data reconstruction based on distance constraint, we employ the residuals of variables to identify the faulty sensor. Since the proposed method does not include iterative calculation, it has a lower calculation burden and is more suitable for online application. The simulation results show that the proposed method effectively identifies the source of typical sensor faults.

Kernel principal component analysis Sensor fault detection and identification Data reconstruction Distance constraint

PENG Hong-xing WANG Rui HAI Lin-peng

School of Computer Science & Technology Henan Polytechnic University Jiaozuo Henan 454000

国际会议

The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

徐州

英文

3857-3862

2010-05-26(万方平台首次上网日期,不代表论文的发表时间)