| COMPUTER SCIENCE AND TECHNOLOGY |
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Cubic causality modeling and uncertain inference method for dynamic fault diagnosis |
| DONG Chunling1, ZHAO Yue2, ZHANG Qin1,2 |
1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China |
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Abstract Complex systems need dynamic, real-time, reliable fault diagnostics but current methods have some shortcomings. This paper expands the dynamic uncertain causality graph method (DUCG) for temporal causality modeling and reasoning theory to correct the limits of the DUCG method and other probabilistic graphical models. A Cubic DUCG is developed that is characterized by a true dynamic model of dynamic problems. The cubic causality graph abandons the restriction of the Markov assumption usually used in temporal models with the fault formation, evolution, and development in dynamic systems represented by allowing causal connections to penetrate among any number of time-slices. The negative feedback dynamics is modelled intuitively combined with a reliable dynamic inference algorithm. Fault tests on the secondary loop of Ningde Nuclear Power Plant Unit 1 (CPR1000) simulator show that Cubic DUCG is accurate, efficient, and capable of dealing with the complex dynamics including negative feedback.
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| Keywords
fault diagnosis
temporal causality modeling
probabilistic reasoning
dynamics and uncertainties
dynamic negative feedback
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Issue Date: 15 July 2018
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2018,
Vol. 58
