关键词: 贝叶斯网络, 故障路径追溯, 概率推理, 最大条件概率

Abstract:

Fault diagnosis techniques based on probabilistic graphical models are often used for uncertain information reasoning. Among them, Bayesian network, an effective tool which has strong characteristics of practicality and applicability, is used extensively. For tracking fault propagation paths in industrial systems with large volume data, this paper proposes a Bayesian-network-based algorithm for fault path tracking. Given all possible combinations of parents, values of specific symptom child node with the maximum conditional probability are estimated by using   decomposition of conditional probability as well as Dichotomy after Bayesian network training. Then the most probable cause is determined by comparing estimations with the observed data. Through layer-by-layer analysis, fault propagation paths in the network can be finally tracked. The experimental results demonstrate that the novel approach is capable of tracking fault paths effectively, which provides higher adaptability and faster speed.

Key words: Bayesian network, fault path tracking, probability reasoning, maximum conditional probability