Readings in model-based diagnosis
Readings in model-based diagnosis
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
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There exists a challenge, i.e., to diagnose failures of such a complex system that has the following characters: (1) it has a causality loop structure; (2) system observed variables are discrete, or continuous, or mixed; and (3) system has time lag, i.e., it has delay of fault effect. For the task, this paper proposes a fault diagnosis prototype system based on causality diagram, and describes the key technologies used. The proposed prototype system comprises datum collection subsystem, model design subsystem, fault diagnosis subsystem, and diagnosis result display subsystem. In this system, the fault knowledge related fault diagnosis is represented in a fault influence propagation diagram (FIPD), which results from a causality diagram. The fault knowledge related delay of fault effect is represented in a 2-time-slice causality diagram (2-TSCD), which is a time stochastic model extended from a FIPD. The method of find fault modes in a FIPD or a 2-TSCD is presented. The proposed prototype system has flexible fault knowledge representation, rapid diagnosis process, and well practicability.