Fusion of soft and hard computing for fault diagnosis in manufacturing systems

The rapid diagnosis of faults in computerized manufacturing systems is crucial to reduce expensive downtime. Many hard computing approaches use either symptom-based or functional reasoning. Symptom-based approaches are unable to handle exceptions, while functional approaches are computationally expensive, and, thus unable to produce a real-time response. Current hybrid approaches which combines the two hard computing methods are too structured in their approach to switching between reasoning methods and, thus fail to provide rapid response comparable to humans. This paper presents a robust, extensible approach to fault diagnosis combines these hard computing methods with the soft computing of agents using fuzzy logic. This fusion of hard and soft computing methods allows unstructured switching between reasoning methods by utilizing multiple intelligent agents which examine the problem domain from a variety of perspectives.