Instrument fault detection and isolation: state of the art and new research trends

Author

Betta, Giovanni ; Pietrosanto, Antonio

Author_Institution

Dept. of Autom., Electromagn., Inf. Eng., & Ind. Math., Cassino Univ., Italy

Volume

49

Issue

1

fYear

2000

fDate

2/1/2000 12:00:00 AM

Firstpage

100

Lastpage

107

Abstract

This paper presents the current state-of-the-art of residual generation techniques adopted in instrument fault detection and isolation. Both traditional and innovative methods are described with their advantages and their limits. The improvement of analytical redundancy technique performances for better dealing with high-dynamics systems and/or with online applications is pointed out as the most interesting need to focus the research efforts

Keywords

Kalman filters; belief networks; diagnostic expert systems; fault diagnosis; fault tolerance; instruments; measurement systems; modelling; neural nets; observers; redundancy; reviews; sensor fusion; ANN; Bayesian networks; Kalman filters; Luemberger observers; analytical redundancy technique performance; automatic measurement systems; data fusion; expert systems; fault location; high-dynamics systems; instrument fault detection; instrument fault isolation; membership functions; modelling; online applications; parity relations; residual generation techniques; sensor systems; state estimators; Automatic control; Calibration; Control systems; Costs; Expert systems; Fault detection; Instruments; Performance analysis; Redundancy; Sensor systems;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.836318

Filename

836318