Model base fault detection and diagnosis methods

Abstract

For the fault detection of technical processes different methods can be applied based on the information extracted from direct measured signals, from signal models and process models. Examples for signal model based fault detection methods are spectral analysis or parameter estimation of ARMA models, examples for process model based methods are parameter estimation, state estimation or parity equation approaches. A comparison of these methods shows that they have different properties with regard to the detection of faults in the process, the actuators and the sensors. By a proper integration of different fault detection methods mainly their advantages can be used to generate a number of different analytical symptoms. For fault diagnosis a knowledge based procedure is required, because also qualitative information in form of heuristic symptoms have to be taken into account. Based on heuristic process knowledge as fault-symptom causalities and a unified representation of all symptoms an integrated fault diagnosis can be performed. This comprises the treatment of the symptoms as uncertain facts and approximate diagnostic reasoning via if-then rules either in a probabilistic or a fuzzy-logic (possibilistic) frame. The described methodology was verified by experiments with several technical processes like electric motors, actuators, pumps, machine tools, robots, heat exchangers, combustion engines and vehicles.