DocumentCode :
784836
Title :
A neural network approach to instrument fault detection and isolation
Author :
Bernieri, Andrea ; Betta, Giovanni ; Pietrosanto, Antonio ; Sansone, Carlo
Author_Institution :
Dept. of Ind. Eng., Cassino Univ., Italy
Volume :
44
Issue :
3
fYear :
1995
fDate :
6/1/1995 12:00:00 AM
Firstpage :
747
Lastpage :
750
Abstract :
An instrument fault detection and isolation (IFDI) technique based on the use of an artificial neural network (ANN) is proposed. The ANN input layer is fed by instrument outputs, and its output layer gives information for instrument diagnosis. The method adopted is described in detail and tested on a complex automatic measurement station for induction motor testing. The performance of the proposed IFDI scheme is experimentally evaluated mainly in terms of correct diagnosis, incorrect fault isolation, missed fault detection, and false alarms. The proposed diagnostic scheme performs well also out of the domain in which it was trained
Keywords :
computerised instrumentation; fault diagnosis; fault location; induction motors; machine testing; neural nets; ANN; IFDI scheme; artificial neural network; correct diagnosis; false alarms; incorrect fault isolation; induction motor testing; input layer; instrument fault detection and isolation; missed fault detection; neural network; Artificial neural networks; Associate members; Automatic testing; Fault detection; Fault diagnosis; Helium; Induction motors; Instruments; Neural networks; Redundancy;
fLanguage :
English
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9456
Type :
jour
DOI :
10.1109/19.387323
Filename :
387323
Link To Document :
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