Knowledge-Based Structures for Fault Diagnosis and Its Applications

Abstract

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper describes structures for advanced methods of fault detection and diagnosis. It begins with a 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 the fault detection and diagnosis of Dieselengines.