The use of genetic algorithms for advanced instrument fault detection and isolation schemes

Author

Betta, Giovanni ; Liguori, Consolatha ; Pietrosanto, Antonio

Author_Institution

Dept. of Ind. Eng., Cassino Univ., Italy

Volume

2

fYear

1996

fDate

1996

Firstpage

1129

Abstract

An advanced scheme for Instrument Fault Detection and Isolation is proposed. It is mainly based on Artificial Neural Networks (ANNs), organized in layers and handled by knowledge-based analytical redundancy relationships. ANN design and training is performed by genetic algorithms which allow ANN architecture and parameters to be easily optimized. The diagnostic performance of the proposed scheme is evaluated with reference to a measurement station for automatic testing of induction motors

Keywords

automatic test equipment; automatic test software; diagnostic expert systems; fault diagnosis; genetic algorithms; induction motors; learning (artificial intelligence); machine testing; neural net architecture; redundancy; ANN architecture; ANN design and training; advanced scheme; automatic testing; data acquisition system; diagnostic performance; fitness function; genetic algorithms; induction motors; instrument fault detection and isolation; knowledge-based analytical redundancy relationships; measurement station; Algorithm design and analysis; Artificial neural networks; Biological cells; Design optimization; Electrical fault detection; Fault detection; Genetic algorithms; Induction motors; Instruments; Redundancy;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 1996. IMTC-96. Conference Proceedings. Quality Measurements: The Indispensable Bridge between Theory and Reality., IEEE

Conference_Location

Brussels

Print_ISBN

0-7803-3312-8

Type

conf

DOI

10.1109/IMTC.1996.507340

Filename

507340