Abstract: Model-based diagnosis concerns using a model of the structure and behavior of a system or device in order to establish why the system or device is faulty. But the fact is that determining a diagnosis for a problem always involves uncertainty. This situation is not entirely satisfactory. This paper built upon and extended previous work in model-based diagnosis by supplementing the model-based framework with probabilistic sound ways for dealing with uncertainty. This was done in a mathematically way in I3ayesian theory to compute the posterior probability that a certain component is not working correctly given some diagnosis. And in this paper we proposed a general method to increase efficiency. The complexity and the completeness of the method were analyzed. I}he time complexity and the space complexity were reduced in the improved method. The experimental results illustrate that the improved method has a better executive efficicncy than the traditional method in general. In fact, the executive efficiency may be improved up to two orders of magnitude in some cases.

Key words: Model-based diagnosis,Bayesian theory,Consistency-based diagnosis