The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper describes advanced methods of fault detection and diagnosis. It begins with a considerationof 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 onparameter estimation, state estimation and parity equations. By comparison with the normal behavior, analytic symptoms are generated. Human operators may be a further source of information, and support the generation of heuristic symptoms. For faultdiagnosis, 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 applicationof these methods is shown for fault detection and diagnosis of a wheel suspension of a vehicle and an electro-mechanical actuator.
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