A hybrid diagnostic system with learning capabilities

Abstract Fast and accurate diagnosis of faults in computer integrated manufacturing systems is essential in order to avoid excessive equipment downtime, and to take full advantage of these systems. Traditional approaches to diagnosis have yielded to artificial intelligence approaches over recent years, as system complexity has increased; but results have been mixed. Symptom-based approaches have been too limited, while structural-based approaches have required excessive computational resources. This paper presents a hybrid model for diagnostics that is computationally efficient, and at the same time incorporates the potential to improve its performance with use through a learning scheme.

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