A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Chun-Ling Dong; Qin Zhang; Shi-Chao Geng

doi:10.1007/s11633-014-0791-8

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

doi: 10.1007/s11633-014-0791-8

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China;
School of Computer Science and Engineering, Beihang University, Beijing 100191, China;
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China

基金项目:

This work was supported by the National Natural Science Foundation of China (Nos. 61050005 and 61273330), Research Foundation for the Doctoral Program of China Ministry of Education (No. 20120002110037), the 2014 Teaching Reform Project of Shandong Normal University, and Development Project of China Guangdong Nuclear Power Group (No. CNPRI-ST10P005).

详细信息

作者简介:
Shi-Chao Geng received his B. Sc. and M. Sc. degrees from Jinan University, China in 2006 and 2010, respectively. He is currently a Ph. D. candidate at School of Computer Science and Engineering, Beihang University. His research interests include uncertain artificial intelligence and graphical model. E-mail: gengsc@hotmail.com

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China;
School of Computer Science and Engineering, Beihang University, Beijing 100191, China;
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China

Funds:

摘要: Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations. However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engi-neering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.
- Fault diagnosis /
- causality model /
- probabilistic graphical model /
- uncertain knowledge representation /
- weighted logic inference
Abstract: Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations. However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engi-neering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.
- Fault diagnosis /
- causality model /
- probabilistic graphical model /
- uncertain knowledge representation /
- weighted logic inference

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A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

doi: 10.1007/s11633-014-0791-8

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

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A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

doi: 10.1007/s11633-014-0791-8

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China;

School of Computer Science and Engineering, Beihang University, Beijing 100191, China;

Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China

English Abstract

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China;

School of Computer Science and Engineering, Beihang University, Beijing 100191, China;

Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China

全文HTML

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A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

doi: 10.1007/s11633-014-0791-8

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

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出版历程

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

doi: 10.1007/s11633-014-0791-8

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China; School of Computer Science and Engineering, Beihang University, Beijing 100191, China; Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China

English Abstract

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China; School of Computer Science and Engineering, Beihang University, Beijing 100191, China; Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China

全文HTML

目录

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China;

School of Computer Science and Engineering, Beihang University, Beijing 100191, China;

Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China

Department of Geographical Information System, Shandong Normal University, Jinan 250014, China;

School of Computer Science and Engineering, Beihang University, Beijing 100191, China;

Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100083, China