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