DocumentCode :
2887439
Title :
Artificial intelligence approaches to fault diagnosis
Author :
Patton, R.J. ; Lopez-Toribio, C.J. ; Uppal, F.J.
Author_Institution :
Sch. of Eng., Hull Univ., UK
fYear :
1999
fDate :
1999
Firstpage :
42491
Lastpage :
518
Abstract :
Fault diagnosis of control engineering systems can be based upon the generation of signals which reflect inconsistencies between the fault-free and faulty system operation-so-called residual signals. This paper outlines some recent approaches to the generation of residual signals using methods of integrating quantitative and qualitative system knowledge, based upon AI techniques
Keywords :
reviews; MIMO system; artificial intelligence approaches; control engineering systems; decision making; dynamic systems; fault diagnosis; fault isolation; fault tolerant control; fault-free operation; faulty system operation; fuzzy inference modelling; fuzzy logic; generation of signals; inconsistencies; neural networks; neuro-fuzzy systems; nonlinear systems; observers; parameter estimation; parity relations; qualitative system knowledge; quantitative system knowledge; residual signals; state space models;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Condition Monitoring: Machinery, External Structures and Health (Ref. No. 1999/034), IEE Colloquium on
Conference_Location :
Birmingham
Type :
conf
DOI :
10.1049/ic:19990188
Filename :
772132
Link To Document :
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