分散型故障診断におけるアークの重要度指標を用いたBayesian Networkの構造構築

抄録

This paper presents construction strategy of Bayesian Network (BN) structures in decentralized fault diagnosis of event-driven systems based on probabilistic inference. In this decentralized diagnosis method, a fault is identified using the BN and Timed Markov Model (TMM). The BN represents the causal relationship between the faults and the observed event sequences in subsystems, and the structure of the BN is essential since the computational complexity and the fault diagnosis performance depend on it. Therefore, this paper proposes construction strategy of the BN based on an importance indicator of arc, which expresses independence properties between faults and observations, in fault diagnosis of event-driven systems. Finally, the usefulness of the proposed strategy is verified through some experimental results of an automatic transfer line simulated on a PC.