首页|期刊导航|高技术通讯（英文版）|Probabilistic SDG model description and fault inference for large-scale complex systems
高技术通讯（英文版）2006，Vol.12Issue(3)：239-244,6.
Probabilistic SDG model description and fault inference for large-scale complex systems
Probabilistic SDG model description and fault inference for large-scale complex systems
摘要
Abstract
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.关键词
signed directed graph (SDG)/hazard assessment/fault diagnosis/Bayesian networkKey words
signed directed graph (SDG)/hazard assessment/fault diagnosis/Bayesian network分类
信息技术与安全科学引用本文复制引用
Yang Fan ,Xiao Deyun..Probabilistic SDG model description and fault inference for large-scale complex systems[J].高技术通讯（英文版）,2006,12(3):239-244,6.基金项目
Supported by the High Technology Research and Development Programme of China (No. 2003AA412310). （No. 2003AA412310）
