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-  2016 

基于贝叶斯网络的故障诊断系统性能评价
Performance evaluation of fault diagnosis system based on Bayesian network

DOI: 10.13700/j.bh.1001-5965.2015.0070

Keywords: 贝叶斯网络(BN),诊断,性能,准确度,置信区间
Bayesian network (BN)
,diagnosis,performance,accuracy,confidence interval

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Abstract:

摘要 故障诊断系统的性能评价是开发和验收故障诊断系统不可或缺的重要环节.针对基于贝叶斯网络(BN)故障诊断系统的性能评价需要,考虑系统诊断结果真实分布,提出采用二项分布参数估计方法来计算诊断准确度的置信区间,采用准确度期望值及其置信区间全面客观评价诊断模型的性能,形成贝叶斯网络模型诊断能力的量化评价指标,为诊断结果的可接受、可信程度以及诊断模型的训练充分性提供参考依据.最后通过燃油系统故障诊断实例验证所述性能评价的有效性.
Abstract:Assessing whether a newly developed fault diagnosis system is effective is an important issue to ensure diagnosis system performance.Due to the requirement of evaluating the performance of the fault diagnosis system based on Bayesian network (BN), an evaluation method using a modified binomial distribution was developed, considering the real distribution of diagnosis results. The parameters of the modified binomial distribution were estimated using training data during the training process of fault diagnosis system, and both diagnosis accuracy and confidence interval of a diagnostic system could be calculated simultaneously by this evaluation method. The quantitive evaluation indices provided by the proposed evaluation method greatly contributed to the evaluation of acceptability and reliability of a Bayesian network-based diagnosis system, and were of great significance in supporting diagnosis system training. In conclusion, the effectiveness of the proposed evaluation method was validated by an example concerning a fault diagnosis system for the aircraft fuel system.

References

[1]  LUXH?J J T,COIT D W.Modeling low probability/high consequence events:An aviation safety risk model[C]//Proceedings of the Reliability & Maintainability Symposium (RAMS).Washington,D.C.:IEEE Computer Society,2006:215-220.
[2]  徐璡,许朝霞,许文杰,等.基于贝叶斯网络原理的835例冠心病病例中医证候分类研究[J].上海中医药杂志,2014,48(1):10-13. XU J,XU Z X,XU W J,et al.Classification of TCM syndromes in 835 cases of coronary heart disease:On the basis of Bayesian networks principle[J].Shanghai Journal of Traditional Chinese Medicine,2014,48(1):10-13(in Chinese).
[3]  DALLA V L,GIUDICI P.A Bayesian approach to estimate the marginal loss distributions in operational risk management[J].Computational Statistics,2008,52(6):3107-3127.
[4]  BASIR O,YUAN X H.Engine fault diagnosis based on multisensor information fusion using Dempster-Shafer evidence theory[J].Information Fusion,2007,8(4):379-386.
[5]  李业波,李秋红,黄向华,等.航空发动机气路部件故障融合诊断方法研究[J].航空学报,2014,35(6):1612-1622. LI Y B,LI Q H,HUANG X H,et al.Research on gas fault fusion diagnosis of aero-engine component[J].Acta Aeronautica et Astronautica Sinica,2014,35(6):1612-1622(in Chinese).
[6]  DAS S,HARRIS M.Estimating accuracy and confidence interval of an intelligent diagnostic reasoner system[C]//Proceedings of the 2009 IEEE Autotestcon.Piscataway,NJ:IEEE Press,2009:288-291.
[7]  WINTERBOTTOM A.The interval estimation of system reliability from component test data[J].Operations Research,1984,32(3):628-640.
[8]  CHOI A,DARWICHE A,ZHENG L,et al.Machine learning and knowledge discovery for engineering systems health management[M].Boca Raton,FL:Chapman and Hall/CRC Press,2011:39-66.
[9]  段荣行,董德存,赵时旻.采用动态故障树分析诊断系统故障的信息融合法[J].同济大学学报(自然科学版),2011,39(11):1699-1704. DUAN R X,DONG D C,ZHAO S M.Information fusion method for system fault[J].Journal of Tongji University(Natural Science),2011,39(11):1699-1704(in Chinese).
[10]  ZHANG J J.Empirical likelihood ratio confidence interval for positively associated series[J].Acta Mathematicae Applicatae Sinica,English Series,2007,23(2):245-254.
[11]  李俭川,胡茑庆,秦国军,等.贝叶斯网络理论及其在设备故障诊断中的应用[J].中国机械工程,2003,14(10):896-900. LI J C,HU N Q,QIN G J,et al.Bayesian network theory and its application in equipment fault diagnosis[J].China Mechanical Engineering,2003,14(10):896-900(in Chinese).
[12]  李鸿.二项分布的参数估计问题研究[J].应用数学学报,2010,33(3):385-394. LI H.The research about binomial distribution parameter estimation problem[J].Acta Mathematicae Applicatae Sinica,2010,33(3):385-394(in Chinese).
[13]  孟昭为.二项分布参数的置信区间[J].工科数学,1995,11(4):169-171. MENG Z W.The confidence interval of the binomial distribution parameters[J].Journal of Mathematics for Technology,1995,11(4):169-171(in Chinese).
[14]  蒋灵,何小荣.BP神经网络的置信度分析[J].计算机与应用化学,1999,16(3):55-60. JIANG L,HE X R.Confidence bounds prediction for backpropagation neural network[J].Computers and Applied Chemsitry,1999,16(3):55-60(in Chinese).
[15]  KOHAVI R.A study of cross-validation and bootstrap for accuracy estimation and model selection[C]//Proceedings of the 14th International Joint Conference in Artificial Intelligence,Volof the International Joint Conference in Artificial Intelligence.San Francisco,CA:Morgan Kaufmann Publishers Inc.,1995,2:1137-1143.
[16]  王正武,任喜风,张瑞平.置信区间分析法在BP神经网络中的应用研究[J].数理统计与管理,2006,25(2):156-160. WANG Z W,RENG X F,ZHANG R P.The research about the application of the confidence interval analysis in BP neural network[J].Application of Statistics and Management,2006,25(2):156-160(in Chinese).
[17]  MADSEN A L,LANG M,KJARULFF U B,et al.The Hugin Tool for learning Bayesian networks[M].Symbolic and Quantitative Approaches to Reasoning with Uncertainty.Berlin:Springer,2003:594-605.
[18]  HECKERMAN D.Learning in graphical models[M].Berlin:Springer Netherlands,1998:301-354.

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