| Probabilistic SDG model description and fault inference for large-scale complex systems |
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| 引用本文： | 杨帆 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 |
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| 作者姓名： | 杨帆 Xiao Deyun |
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| 作者单位： | Department of Automation, Tsinghua University, Beijing 100084, P.R. China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 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 con- struction 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.
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| 关 键 词： | 危害评价 SDG 故障诊断 贝叶斯网络 复杂系统 |
| 收稿时间： | 2005-05-31 |
Probabilistic SDG model description and fault inference for large-scale complex systems |
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| Yang Fan,Xiao Deyun. Probabilistic SDG model description and fault inference for large-scale complex systems[J]. High Technology Letters, 2006, 12(3): 239-244 |
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| Authors: | Yang Fan Xiao Deyun |
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| 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. |
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| Keywords: | signed directed graph (SDG) hazard assessment fault diagnosis Bayesian network |
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