abstract

The AI model-based diagnosis community has developed qualitative reasoning mechanisms for fault isolation in dynamic systems. Their emphasis has been on the fault isolation algorithms, and little attention has been paid to robust online detection and symbol generation that are essential components of a complete diagnostic solution. This paper discusses a robust diagnosis methodology for hybrid systems that combines fault detection with a combined qualitative and quantitative fault isolation scheme. We focus on fault detection, symbol generation, and parameter estimation, and illustrate the effectiveness of this method by running experiments on the fuel transfer system of aircraft.

Download (isis main website): Biswas__G_6_0_2003_A_robust_m.pdf

Copyright 2003 IFAC.