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	Probabilistic SDG model description and fault inference for large-scale complex systems
	Yang Fan ; Xiao Deyun
	2010-05-06 ; 2010-05-06
关键词	Practical Theoretical or Mathematical Experimental/ belief networks inference mechanisms large-scale systems/ signed directed graph probabilistic SDG model fault computing large-scale complex system Bayesian inference junction tree algorithm/ C1230R Reasoning and inference in AI C6170K Knowledge engineering techniques
中文摘要	Large-scale complex systems have the feature of including large amount of variables that have complex relationships, for which signed directed graph (SDG) model could serve as a significant tool by describing the causal relationships among variables. Although qualitative SDG expresses the causing effects between variables easily and clearly, it has many disadvantages or limitations. Probabilistic SDG proposed in the article describes deliver relationships among faults and variables by conditional probabilities, which contains more information and performs more applicability. The article introduces the concepts and construction approaches of probabilistic SDG, and presents the inference approaches aiming at fault diagnosis in this framework, i.e. Bayesian inference with graph elimination or junction tree algorithms to compute fault probabilities. Finally, the probabilistic SDG of a typical example of 65t/h boiler system is given.
语种	英语 ; 英语
出版者	Editorial Dept. High Technol. Lett ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9338]
专题	清华大学
推荐引用方式 GB/T 7714	Yang Fan,Xiao Deyun. Probabilistic SDG model description and fault inference for large-scale complex systems[J],2010, 2010.
APA	Yang Fan,&Xiao Deyun.(2010).Probabilistic SDG model description and fault inference for large-scale complex systems..
MLA	Yang Fan,et al."Probabilistic SDG model description and fault inference for large-scale complex systems".(2010).

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