An advanced neural-network-based instrument fault detection and isolation scheme

Author

Betta, Giovanni ; Liguori, Consolatina ; Pietrosanto, Antonio

Author_Institution

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

Volume

47

Issue

2

fYear

1998

fDate

4/1/1998 12:00:00 AM

Firstpage

507

Lastpage

512

Abstract

An advanced scheme for instrument fault detection and isolation is proposed. It is based on artificial neural networks (ANN´s), 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 testing; fault diagnosis; genetic algorithms; induction motors; machine testing; neural nets; redundancy; IFDI; artificial neural network; automatic testing; diagnostic instrument; fault detection; fault isolation; genetic algorithm; induction motor; knowledge-based system; measurement station; redundancy; Algorithm design and analysis; Artificial neural networks; Associate members; Automatic testing; Design optimization; Fault detection; Genetic algorithms; Induction motors; Instruments; Redundancy;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.744199

Filename

744199