A comparison of model-based reasoning and learning approaches to power transmission fault diagnosis

An application of model-based reasoning and model-based learning to an operative diagnostic domain such as electrical power transmission networks is presented. Most of the research in model-based diagnosis is based on maintenance diagnosis. Operative diagnosis, on the other hand, is done while the system is still in operation even after the fault. We plan to develop an efficient algorithm for operative diagnosis which can handle a large domain of faults and multiple faults in real time. In our search toward a better algorithm, we develop and compare two different reasoning methods: diagnosis based on model based reasoning, and diagnosis based on heuristic rules learnt from model based reasoning. This paper presents the results of the comparison.