A symbolic reasoning approach to fault detection and isolation applied to electrical machines

This paper describes a model-based fault isolation method using fuzzy logic and applied it to the diagnosis of a DC motor current control loop. The global model is divided into multiple local models. Instead of using crisp thresholds to detect an abnormal state of the system, fuzzy information processing is implemented, which allows the fusion of various information (numerical or symbolic) in the decision-making procedure, and thus improves the isolation ability.