DocumentCode :
2518555
Title :
A neural network approach for identification and fault diagnosis on dynamic systems
Author :
Bernier, A. ; D´Apuzzo, M. ; Sansone, L. ; Savastano, M.
Author_Institution :
Dipartimento di Ingegneria Ind., Cassino Univ., Italy
fYear :
1993
fDate :
18-20 May 1993
Firstpage :
564
Lastpage :
569
Abstract :
The possibilities offered by neural networks for overcoming both system identification and fault diagnosis problems in dynamic systems are investigated. In particular, an original neural fault diagnosis procedure is illustrated. Its sensitivity and response time enables it to be used to great advantage in online applications. Some applications are also reported which, although pertaining to a simple linear dynamic system, highlight the general applicability and advantages of a neural approach
Keywords :
automatic test equipment; fault diagnosis; fault location; identification; neural nets; dynamic systems; fault diagnosis; identification; neural network; online applications; response time; sensitivity; Artificial neural networks; Availability; Biological neural networks; Delay; Fault diagnosis; Feedforward systems; Humans; Neural networks; Nonlinear dynamical systems; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1993. IMTC/93. Conference Record., IEEE
Conference_Location :
Irvine, CA
Print_ISBN :
0-7803-1229-5
Type :
conf
DOI :
10.1109/IMTC.1993.382579
Filename :
382579
Link To Document :
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