FDI with computer-assisted human intelligence

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 is used to detect and isolate faults. The quantitative part is used to generate fault symptoms (residuals) using a quantitative (mathematical) model of the process. These residuals contain the information about whether a fault has occurred 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 and 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 is presented in the paper