动态故障诊断中的立体因果建模与不确定性推理方法

董春玲; 赵越; 张勤

doi:10.16511/j.cnki.qhdxxb.2018.26.029

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清华大学学报（自然科学版） ›› 2018, Vol. 58 ›› Issue (7) : 614-622. DOI: 10.16511/j.cnki.qhdxxb.2018.26.029

计算机科学与技术

动态故障诊断中的立体因果建模与不确定性推理方法

董春玲¹, 赵越², 张勤^1,2

作者信息 +

Cubic causality modeling and uncertain inference method for dynamic fault diagnosis

DONG Chunling¹, ZHAO Yue², ZHANG Qin^1,2

Author information +

文章历史 +

摘要

为满足复杂系统的动态、实时和高可靠性的故障诊断需求，克服动态不确定因果图（dynamic uncertain causality graph，DUCG）及其他概率图模型的局限，该文在DUCG理论的基础上扩展其时序因果表达与推理方法，建立了立体DUCG （Cubic DUCG）理论模型。采用动态的手段处理动态问题，以"逐步生长"的立体因果建模取消了时序模型中常见的Markov假设限制，以穿越式因果连接准确地表达动态系统下故障的产生、演变和发展；直观地刻画和处理动态负反馈等复杂故障逻辑因果关系；给出了严谨、高效的动态推理算法。宁德核电站1号机组CPR1000模拟机二回路系统上的故障实验结果表明：Cubic DUCG诊断推理准确、高效，能有效处理负反馈等复杂动态情形。

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.

导出引用

董春玲, 赵越, 张勤. 动态故障诊断中的立体因果建模与不确定性推理方法[J]. 清华大学学报（自然科学版）. 2018, 58(7): 614-622 https://doi.org/10.16511/j.cnki.qhdxxb.2018.26.029

DONG Chunling, ZHAO Yue, ZHANG Qin. Cubic causality modeling and uncertain inference method for dynamic fault diagnosis[J]. Journal of Tsinghua University(Science and Technology). 2018, 58(7): 614-622 https://doi.org/10.16511/j.cnki.qhdxxb.2018.26.029

参考文献

[1] HELLER K A, TEH Y W, G R R D, UNIT G. Infinite hierarchical hidden Markov models[J]. Journal of Machine Learning Research, 2009, 5:224-231.
[2] PETROPOULOS A, CHATZIS S P, XANTHOPOULOS S. A hidden Markov model with dependence jumps for predictive modeling of multidimensional time-series[J]. Information Sciences, 2017, 412-413:50-66.
[3] SERIR L, RAMASSO E, ZERHOUNI N. Time-sliced temporal evidential networks:The case of evidential HMM with application to dynamical system analysis[C]//Proceedings of 2011 IEEE Conference on Prognostics and Health Management (PHM). Montreal, Canada:IEEE, 2011:1-10.
[4] SCHOCKAERT S, DE COCK M. Temporal reasoning about fuzzy intervals[J]. Artificial Intelligence, 2008, 172(8-9):1158-1193.
[5] SEZER S, ATALAY A E. Dynamic modeling and fuzzy logic control of vibrations of a railway vehicle for different track irregularities[J]. Simulation Modelling Practice and Theory, 2011, 19(9):1873-1894.
[6] RUAN S, ZHOU Y K, YU F L, et al. Dynamic multiple-fault diagnosis with imperfect tests[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part A:Systems and Humans, 2009, 39(6):1224-1236.
[7] SINGH S, KODALI A, CHOI K, et al. Dynamic multiple fault diagnosis:Mathematical formulations and solution techniques[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part A:Systems and Humans, 2009, 39(1):160-176.
[8] ARROYO-FIGUEROA G, SUCAR L E. Temporal Bayesian network of events for diagnosis and prediction in dynamic domains[J]. Applied Intelligence, 2005, 23(2):77-86.
[9] GALAN S F, ARROYO-FIGUEROA G, DIEZ F J, SUCAR L E. Comparison of two types of event Bayesian networks:A case study[J]. Applied Artificial Intelligence, 2007, 21(3):185-209.
[10] NODELMAN U. Continuous time Bayesian networks[D]. Palo Alto:Stanford University, 2007.
[11] WINGATE D, GOODMAN N D, ROY D M, et al. The infinite latent events model[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. Montreal, Canada:AUAI Press, 2009:607-614.
[12] MURPHY K P. Dynamic Bayesian networks:Representation, inference and learning[D]. Berkeley:University of California, 2002.
[13] CAI B P, LIU Y, XIE M. A dynamic-Bayesian-network-based fault diagnosis methodology considering transient and intermittent faults[J]. IEEE Transactions on Automation Science and Engineering, 2017, 14(1):276-285.
[14] CODETTA-RAITERI D, PORTINALE L. Dynamic Bayesian networks for fault detection, identification, and recovery in autonomous spacecraft[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2015, 45(1):13-24.
[15] ZHANG Q. Dynamic uncertain causal graph for knowledge representation and reasoning:Discrete DAG cases[J]. Journal of Computer Science and Technology, 2012, 27(1):1-23.
[16] ZHANG Q, DONG C L, CUI Y, et al. Dynamic uncertain causality graph for knowledge representation and probabilistic reasoning:Statistics base, matrix, and application[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(4):645-663.
[17] ZHANG Q, GENG S C. Dynamic uncertain causality graph applied to dynamic fault diagnoses of large and complex systems[J]. IEEE Transactions on Reliability, 2015, 64(3):910-927.
[18] ZHANG Q, ZHANG Z. Dynamic uncertain causality graph applied to dynamic fault diagnoses and predictions with negative feedbacks[J]. IEEE Transactions on Reliability, 2016, 65(2):1030-1044.
[19] DONG C L, ZHAO Y, ZHANG Q. Assessing the influence of an individual event in complex fault spreading network based on dynamic uncertain causality graph[J]. IEEE Transactions on Neural Networks and learning systems, 2016, 27(8):1615-1630.
[20] ZHANG Q. Dynamic uncertain causality graph for knowledge representation and reasoning:continuous variable, uncertain evidence, and failure forecast[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2015, 45(7):990-1003.
[21] ZHOU Z X, ZHANG Q. Model event/fault trees with dynamic uncertain causality graph for better probabilistic safety assessment[J]. IEEE Transactions on Reliability, 2017, 66(1):178-188.
[22] DONG C L, WANG Y J, ZHANG Q, et al. The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo[J]. Computer Methods and Programs in Biomedicine, 2014, 113(1):162-174.
[23] 董春玲, 张勤. 用于不确定性故障诊断的权重逻辑推理算法研究[J]. 自动化学报, 2014, 40(12):2766-2781.DONG C L, ZHANG Q. Research on weighted logical inference for uncertain fault diagnosis[J]. Acta Automatica Sinica, 2014, 40(12):2766-2781.(in Chinese)
[24] ZHANG Q, YAO Q Y. Dynamic uncertain causality graph for knowledge representation and reasoning:Utilization of statistical data and domain knowledge in complex cases[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 99:1-15.

基金

国家自然科学基金资助项目（61402266，71671103）；中国博士后科学基金资助项目（2016M590099）

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收稿日期	出版日期
2017-12-01	2018-07-15
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2018-07-15

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