Title :
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
fDate :
2/1/2000 12:00:00 AM
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;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
