Abstract
We propose a methodology to diagnose multiple faults in complex systems. The approach is based on the Independent Choice Logic (ICL) and comprises two phases. In phase 1 we generate the explanations of the observed symptoms and handle the combinatorial explosion with a heuristic. In phase 2 we observe process signals to detect abnormal behavior that can lead us to identify the real faulted components. A proposal is made to automate this task with Dynamic Bayesian Networks (DBNs) embedded in the ICL formalism. The overall scheme is intended to give a definite diagnosis. ICL is a framework, which comprises a theory and a development environment. We show that ICL can be scaled-up to real-world, industrial-strength problems by using it in diagnosing faults in an electrical power transmission network .
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Garza, L.E., Cantú, F., Acevedo, S. (2000). A Methodology for Multiple-Fault Diagnosis Based on the Independent Choice Logic. In: Monard, M.C., Sichman, J.S. (eds) Advances in Artificial Intelligence. IBERAMIA SBIA 2000 2000. Lecture Notes in Computer Science(), vol 1952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44399-1_43
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DOI: https://doi.org/10.1007/3-540-44399-1_43
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