Title :
On PCA-based fault diagnosis techniques
Author :
Yin, Shen ; Steven, X.D. ; Naik, Amol ; Deng, Pengcheng ; Haghani, Adel
Author_Institution :
Inst. of Autom. & Complex Syst., Univ. of Duisburg-Essen, Duisburg, Germany
Abstract :
This paper presents the application of standard PCA technique to fault diagnosis system design. Based on the fault detectability analysis of existed test statistics, the joint use of some test statistics is recommended. Our further study is dedicated to develop a fault isolation approach based on likelihood ratio test, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The issues of off-set and scaling fault identification will be also discussed and the complete scheme of PCA-based fault diagnosis procedure is proposed.
Keywords :
fault diagnosis; principal component analysis; process control; process monitoring; statistical testing; PCA-based fault diagnosis; fault classification; fault detectability analysis; fault diagnosis system design; fault identification; fault isolation; fault scaling; industrial process; likelihood ratio test; off-set fault direction; principal component analysis; process monitoring; test statistics; Covariance matrix; Equations; Fault detection; Fault diagnosis; Indexes; Monitoring; Principal component analysis;
Conference_Titel :
Control and Fault-Tolerant Systems (SysTol), 2010 Conference on
Conference_Location :
Nice
Print_ISBN :
978-1-4244-8153-8
DOI :
10.1109/SYSTOL.2010.5676031