Model-based Diagnosis: A Probabilistic Extension

The present study treats model-based diagnosis as an uncertain reasoning problem. To handle the uncertainty in model-based diagnosis effectively, a probabilistic approach serves as a point of departure. The use of probabilities in diagnosis has proved beneficial to the performance of diagnostic engines.We extend the use of probabilities to reflect the aging processes affecting component lifetimes. Unexpected failures signal unusual operating conditions possibly due to the failure of other subsystems. The diagnostic system architecture proposed here is capable of detecting failures that are difficult to detect using a conventional diagnostic engine. Moreover, ascribing a statistical interpretation to nonmonotonic reasoning, allows us to use a hybrid (probabilistic-logical) inference engine at the heart of this system.