DocumentCode :
3639874
Title :
Principal component analysis (PCA) based fault detection method and experimental applications
Author :
Alkan Alkaya;İlyas Eker
Author_Institution :
Elektrik-Elektronik Mü
fYear :
2010
Firstpage :
189
Lastpage :
192
Abstract :
The fault detection based upon multivariate statistical projection method (such as principal component analysis, PCA) have attracted more and more interest in academic research and engineering practice. PCA methods for fault detection use data collected from a steady-state process to monitor T2 and Q statistics with a calculated control limit. In this paper, PCA and statistical control chart (SCC) have been used to detect process operating sensor and actuator faults on an electromechanical system. Hotelling, T2, statistic is used calculating the control limits of SCC. Experimental results indicate that the method is effective and available.
Keywords :
"Principal component analysis","Mathematical model","Fault tolerance","Fault tolerant systems","DC motors","Fault detection","Process control"
Publisher :
ieee
Conference_Titel :
Electrical, Electronics and Computer Engineering (ELECO), 2010 National Conference on
Print_ISBN :
978-1-4244-9588-7
Type :
conf
Filename :
5698111
Link To Document :
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