Experience-Aided Diagnosis for Complex Devices

Abstract

This paper presents a novel approach to diagnosis which addresses the two problems - computational complexity of abduction and device models - that have prevented model-based diagnostic techniques from being widely used. The Experience-Aided Diagnosis (EAD) model is defined that combines deduction to rule out hypotheses, abduction to generate hypotheses and induction to recall past experiences and account for potential errors in the device models. A detailed analysis of the relationship between case-based reasoning and induction is also provided. The EAD model yields a practical method for solving hard diagnostic problems and provides a theoretical basis for overcoming the problem of partially incorrect device models.