A Fuzzy FDI Decision Making System for the Support of the Human Operator

Abstract

The goal of Fault Detection and Isolation (FDI) is to decide whether and where a fault in the system under consideration has occurred avoiding wrong decisions that cause false alarms. To achieve a fault detection scheme which is robust in the sense of false alarms a combined quantitative/qualitative supervision system will be used to detect and isolate faults. The quantitative part will be used to generate fault symptoms (residuals) using a quantitative (mathematical) model of the process. These residuals contain the information whether a fault has occured or not. The next step in the FDI process is the residual evaluation . There exists a number of different residual evaluation techniques, for example simple threshold logic tests, statistical decision making, pattern recognition, decision making based on fuzzy logic or neural networks. The fundamental difficulty with residual evaluation is that residuals are normally uncertain, corrupted by noise, disturbances and, if the residuals are generated by model-based techniques, by modelling uncertainties. In order to select from the given residual data the important fault information a human support tool for the generation of a knowledge base for fault diagnosis will be presented in this paper.