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贝叶斯网络发展及其应用综述

Keywords: 贝叶斯网络,故障诊断,可靠性分析

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Abstract:

贝叶斯网络(BN)是一种用于描述变量间不确定性因果关系的图形网络模型,用于不确定性系统建模和推理,处理涉及到预测智能推理、诊断、决策风险及可靠性分析的问题.本文首先对贝叶斯网络做了一个简略的介绍,随后综述了贝叶斯网络近30年的发展及功能扩展,对其在工程技术领域的应用包括故障诊断及可靠性分析等方面做了一个回顾,最后对BN现有的不足和未来的研究趋势做了总结和展望.

References

[1]  Pearl J F. Propagation and structuring in belief networks[J]. Artificial Intelligence, 1986, 29(3):241-288.
[2]  Lauritzen S L, Spiegelhalter D J. Local computations with probabilities on graphical structures and their application to expert systems (with discussion)[J]. Journal of the Royal Statistical Society Series B, 1988, 50(2):157-224.
[3]  Shenoy P, Shafer G. Axioms for probability and belief-function propagation/readings in uncertain reasoning[M]. Porland, USA: Morgan Kaufmann Publishers Inc, 1990:575-610.
[4]  厉海涛,金光,周经伦,等.贝叶斯网络推理算法综述[J].系统工程与电子技术,2008,30(5):935-939. Li Haitao,Jin Guang,Zhou Jinglun,et al. Survey of Bayesian network inference algorithms[J]. Systems Engineering and Electronics,2008,30(5):935-939.(in Chinese)
[5]  Hugin Expert. The leading decision support tool[EB/OL].[2012-09-12]. http://www.hugin.com.
[6]  Murphy K. Software packages for graphical models-bayesian networks[EB/OL].[2012-09-12]. http://www.cs.ubc.ca/~murphyk/Bayes/bnsoft.html.
[7]  Koller D, Pfeffer A. Object-oriented Bayesian networks[C][C]//Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence. Providence, Rhode Island:[s.n.], 1997:302-313.
[8]  Fenton N E. The SERENE method manual (safety and risk evaluation using bayesian NEts), EC Project No. 22187 SERENE, SERENE/5.3/CSR/3053/R/1[EB/OL].[2012-09-12]. www.dcs.qmul.ac.uk/~norman/papers/serene.pdf
[9]  Neil M, Fenton N E, Nielsen L. Building large-scale Bayesian networks[J]. The Knowledge Engineering Review, 2000, 15(3):257-284.
[10]  Spirtes P, Glymour C, Scheines R. Causation, prediction, and search[C]//Proceedings of Adaptive Computation and Machine Learning. Boston: MIT Press, 2000.
[11]  Steck H. Constrained-based structural learning in Bayesian networks using finite data sets[D].[S.l]: Institute of der Informatik der Technischen University, 2001.
[12]  Chan H, Darwiche A. A distance measure for bounding probabilistic belief change[J]. International Journal of Approximate Reasoning, 2005, 38(2):149-174.
[13]  Cheng J, Greiner R, Kelly J, et al. Learning Bayesian networks from data: an information-theory based approach[J]. Artificial Intelligence, 2002, 137(1-2):43-90.
[14]  Neil M, Tailor M. Inference in hybrid Bayesian networks using dynamic discretization[J]. Statistics and Computing, 2007, 17(3):219-233.
[15]  Horvitz E, Heckerman D, Nathwani B. Heuristic abstraction in the decision-theoretic pathfinder system[C]//Proceedings of Symposium on Computer Applications in Medical Care. Washington D.C., USA: IEEE, 1989.
[16]  Andreassen S, Woldbye M, Falck B, et al. MUNIN: a causal probabilistic network for interpretation of electromyographic findings[C]//Proceedings of the 10th International Joint Conference on Artificial Intelligence. Milan, Italy: LJCAI Inc, 1987:366-372.
[17]  Breese J, Heckerman D. Decision-theoretic troubleshooting: a framework for repair and experiment[C]//Proceedings of the 12 Conference on Uncertainty in Artificial Intelligence. Porland, USA: Morgon Kaufmann Publisher Inc, 1996:124-132.
[18]  Heckerman D, Breese J S, Rommelse K. Decision-theoretic trouble shooting[J]. Communication of the ACM, 1995, 38(3):49-57.
[19]  Jensen F V, Kjerulff U, Kristiansen B, et al. The SACSO methodology for troubleshooting complex systems[J]. Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2000, 15(4):321-333.
[20]  Huang Y, McMurran R, Dhadyalla G. Probability based vehicle fault diagnosis: Bayesian network method[J]. Journal of Intelligent Manufacture, 2008, 19:301-311.
[21]  Scheiterer R S, Obradovic D, Tresp V. Tailored-to-fit bayesian network modelling of expert diagnostic knowledge[J]. Journal of VLSI Signal Processing, 2007, 49:301-316.
[22]  Xu B G. Intelligent fault inference for rotating flexible rotors using Bayesian belief network[J]. Expert Systems with Applications, 2012, 39(1):816-822.
[23]  朱永利, 王艳.基于贝叶斯网络的电网故障诊断[J].电力自动化设备, 2007, 27(7):33-36. Zhu Yongli, Wang Yan. Power system fault diagnosis based on Bayesian network[J]. Electric Power Automation Equipment, 2007, 27(7):33-36.(in Chinese)
[24]  程延伟, 谢永成, 李光升, 等.基于贝叶斯网络的车辆电源系统故障诊断方法[J].计算机工程, 2011, 37(23):251-253. Cheng Yanwei, Xie Yongcheng, Li Guangsheng, et al. Fault diagnosis method of vehicle power system based on Bayesian network[J]. Computer Engineering, 2011, 37(23):251-253.(in Chinese)
[25]  姜万录, 刘思远. 多特征信息融合的贝叶斯网络故障诊断方法研究[J].中国机械工程, 2010, 21(8):941-945. Jiang Wanglu, Liu Siyuan. Fault diagnosis approach study of Bayesian networks based on multi-characteristic information fusion[J]. China Mechanical Engineering, 2010, 21(8):941-945.(in Chinese)
[26]  赵文清, 朱永利, 王晓辉.基于组合贝叶斯网络的电力变压器故障诊断[J].电力自动化设备, 2009, 29(11):6-9. Zhao Wengqing, Zhu Yongli, Wang Xiaohui. Combinatorial Bayes network in fault diagnosis of power transformer[J]. Electric Power Automation Equipment, 2009, 29(11):6-9.(in Chinese)
[27]  Coleman A, Zalewski J. Intelligent fault detection and diagnostics in solar plants[C]//Proceedings of the 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2011).[S.l.]: IEEE, 2011:948-953.

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