Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks |
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Authors: | Deng Yong & Shi Wenkang School of Electronics & Information Technology Shanghai Jiaotong University Shanghai P. R. China |
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Affiliation: | School of Electronics & Information Technology, Shanghai Jiaotong University, Shanghai 200030, P. R. China |
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Abstract: | In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that "if component m 1 is faulty, then component m 2 may be faulty too". How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge. |
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Keywords: | Model-based diagnosis Experts' knowledge Probabilistic assumption-based reasoning Bayes networks. |
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