【摘 要】
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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
【机 构】
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Jiangsu Frontier Electric Technology Co.Ltd.,Nanjing,211102,China
【出 处】
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第六届中国计算机学会大数据学术会议
论文部分内容阅读
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 identify fault propagation paths in industrial systems with large volume data,a novel BN-based identification algorithm using parent nodes filter is proposed.Given all possible combinations of parents,values of specific symptom child node with the maximum conditional probability are estimated using decomposition of conditional probability as well as Dichotomy after BN 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 traced.Experimental results have demonstrated that the novel approach is capable of identifying fault paths effectively,which provides higher adaptability and faster speed.
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