Abstract
The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This contribution describes advanced methods of fault detection and diagnosis. It begins with the consideration of a knowledge-based procedure, which is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from measured signals and use process and signal models. These methods are based on parameter estimation, state estimation and parity equations. By comparison with the normal behaviour, analytic symptoms are generated. Human operators may be a further source of information and support the generation of heuristic symptoms. For fault diagnosis, all symptoms have to be processed in order to determine possible faults. This can be performed by classification methods or approximate reasoning, using probabilistic or possibilistic (fuzzy) approaches based on if-then rules. The application of these methods is shown for fault detection and diagnosis of a machine tool drive and a d.c.motor. Emphasis is given to the application of fuzzy logic in various parts of the diagnosis system.
Published Version
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