基于多状态贝叶斯网络的导弹质量状态评估
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摘要
针对"是非制"导弹质量状态分类方法过于粗放、信息资源利用不充分的问题,提出了基于DS证据理论-层次分析法的条件概率赋值方法,并构建了贝叶斯网络的质量状态评估模型。在分析性能特征参数的基础上,将导弹状态划分为良好、较好、堪用、拟故障和故障5个等级。对单位不一、阈值各异的测试参数进行归一化处理,以改进的岭形分布函数确定各质量状态等级的隶属度,构建基于贝叶斯网络的导弹质量状态评估模型。鉴于各节点间为非确定性逻辑关系、数据信息相对缺乏,提出基于DS证据理论-层次分析法的多状态贝叶斯网络条件概率值确定方法。以某型导弹为例,经过验证,DS证据理论-层次分析法确定条件概率值的方法将不确定度降低到了5%左右,并通过横向对比与纵向对比确定了该模型的合理性与可信性。
A condition assessment model of missile based on Bayesian network( BN) with conditional probability table( CPT) which is determined by using DS evidence theory and analytic hierarchy process( AHP) is established for the extensive way and inadequate information resources ultilization of traditional state classification method. Missile state is divided into five grades,i. e.,perfect,better,usable,pseudo-fault and fault,based on the analysis of characteristic parameters. Normalization processing is carried out on the test parameters of different units and threshold values,and the modified ridge shape functions are used to determine the degree of membership of all the states. A BN-based condition assessment model of a missile is established. In the view of uncertain logical relationship between nodes and lacking of data,a method is proposed to determine CPT of multi-state Bayesian network on the basis of DS evidence theory and AHP. The result shows that uncertainty is decreased to about five percent by utilizing DS/AHP method to determine CPT. The reasonability and credibility of BN model are verified through horizontal and vertical comparison.
A condition assessment model of missile based on Bayesian network( BN) with conditional probability table( CPT) which is determined by using DS evidence theory and analytic hierarchy process( AHP) is established for the extensive way and inadequate information resources ultilization of traditional state classification method. Missile state is divided into five grades,i. e.,perfect,better,usable,pseudo-fault and fault,based on the analysis of characteristic parameters. Normalization processing is carried out on the test parameters of different units and threshold values,and the modified ridge shape functions are used to determine the degree of membership of all the states. A BN-based condition assessment model of a missile is established. In the view of uncertain logical relationship between nodes and lacking of data,a method is proposed to determine CPT of multi-state Bayesian network on the basis of DS evidence theory and AHP. The result shows that uncertainty is decreased to about five percent by utilizing DS/AHP method to determine CPT. The reasonability and credibility of BN model are verified through horizontal and vertical comparison.
引文
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[2]王少华,张耀辉,韩小孩.基于多维特征参数的装备状态评估方法[J].系统工程与电子技术,2014,36(9):1769-1775.WANG Shao-hua,ZHANG Yao-hui,HAN Xiao-hai.Equipment condition evaluation based on multi-dimensional characteristics parameters[J].Systems Engineering and Electronics,2014,36(9):1769-1775.(in Chinese)
[3]王京,李天梅,何华锋,等.多源测试性综合评估数据等效折合模型与方法研究[J].兵工学报,2017,38(1):151-159.WANG Jing,LI Tian-mei,HE Hua-feng,et al.Research on multisource data equivalent methods for testability integrated evaluation[J].Acta Armamentarii,2017,38(1):151-159.(in Chinese)
[4]王亮,吕卫民,滕克难.基于测试数据的长期贮存装备实时健康状态评估[J].系统工程与电子技术,2013,35(6):1212-1217.WANG Liang,LYU Wei-min,TENG Ke-nan.Real-time health condition assessment of long-term storage equipment based on testing data[J].Systems Engineering and Electronics,2013,35(6):1212-1217.(in Chinese)
[5]丛林虎,徐廷学,董琪,等.基于改进证据理论的导弹状态评估方法[J].系统工程与电子技术,2016,38(1):70-76.CONG Lin-hu,XU Ting-xue,DONG Qi,et al.Missile condition assessment method based on improved evidence theory[J].System Engineering and Electronics,2016,38(1):70-76.(in Chinese)
[6]蒋金良,袁金晶,欧阳森.基于改进隶属度函数的电能质量模糊综合评价[J].华南理工大学学报:自然科学版,2012,40(11):107-112.JIANG Jin-liang,YUAN Jin-jing,OUYANG Sen.Fuzzy comprehensive evaluation of power quality based on improved membership function[J].Journal of South China University of Technology:Natural Science Edition,2012,40(11):107-112.(in Chinese)
[7]周忠宝,董豆豆,周经伦.贝叶斯网络在可靠性分析中的应用[J].系统工程理论与实践,2006,26(6):95-100.ZHOU Zhong-bao,DONG Dou-dou,ZHOU Jing-lun.Application of Bayesian network in reliability analysis[J].System Engineering Theory&Practice,2006,26(6):95-100.(in Chinese)
[8]尹晓伟,钱文学,谢里阳.基于贝叶斯网络的多状态系统可靠性建模与评估[J].机械工程学报,2009,45(2):206-212.YIN Xiao-wei,QIAN Wen-xue,XIE Li-yang.Multi-state system reliability mdeling and assessment based on Bayesian networks[J].Journal of Mechanical Engineering,2009,45(2):206-212.(in Chinese)
[9]Beynon M.DS/AHP method:a mathematical analysis,including an understanding of uncertainty[J].European Journal of Operational Research,2002,140(1):148-164.
[10]杜元伟,石方园,杨娜.基于证据理论/层次分析法的贝叶斯网络建模方法[J].计算机应用,2015,35(1):140-146.DU Yuan-wei,SHI Fang-yuan,YANG Na.Construction method for Bayesian network based on Dempster-Shafer/analytic hierarchy process[J].Journal of Computer Applications,2015,35(1):140-146.(in Chinese)
[11]Hua Z S,Gong B G,Xu X Y.A DS-AHP approach for multi-attribute decision making problem with incomplete information[J].Expert Systems with Applications,2008,34(3):2221-2227.
[12]姚爽,郭亚军,黄玮强.基于证据距离的改进DS-AHP多属性群决策方法[J].控制与决策,2010,25(6):894-898.YAO Shuang,GUO Ya-jun,HUANG Wei-qiang.An improved method of aggregation in DS/AHP for multi-criteria group decision-making based on distance meaaasure[J].Control and Decision,2010,25(6):894-898.(in Chinese)
[13]姜江.证据网络模型及其推理算法[J].系统工程理论与实践,2015,35(4):984-990.JIANG Jiang.Evidential network model and reasoning approach[J].System Engineering—Theory&Practice,2015,35(4):984-990.(in Chinese)
[14]孙伟超,李文海,李文峰.融合粗糙集与D-S证据理论的航空装备故障诊断[J].北京航空航天大学学报,2015,41(10):1902-1909.SUN Wei-chao,LI Wen-hai,LI Wen-feng.Avionic devices fault diagnosis based on fusion method of rough set and D-S theory[J].Journal of Beijing University of Aeronautics and Astronautics,2015,41(10):1902-1909.(in Chinese)
[15]黄影平.贝叶斯网络发展及其应用综述[J].北京理工大学学报,2013,33(12):1211-1219.HUANG Ying-ping.Survey on Bayesian network development and application[J].Transactions of Beijing Institude of Technology,2013,33(12):1211-1219.(in Chinese)
[16]Wilson A G,Huzurbazar A V.Bayesian networks for multilevel system reliability[J].Reliability Engineering&System Safety,2007,92(10):1413-1420.
[17]Simon C,Weber P,Evsukoff A.Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis[J].Reliability Engineering&System Safety,2008,93(7):950-963.
[18]Piero B,Luca P,Lusine M,et al.Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application[J].Reliability Engineering&System Safety,2015,138:176-193.
[19]Masegosa A R,Moral S.An interactive approach for Bayesian network learning using domain/expert knowledge[J].International Journal of Approximate Reasoning,2013,54(8):1168-1181.
[20]锁斌,曾超,程永生,等.证据理论与贝叶斯网络相结合的可靠性分析方法[J].系统工程与电子技术,2011,33(10):2343-2347.SUO Bin,ZENG Chao,CHENG Yong-sheng,et al.Reliability analysis based on evidence theory and Bayesian network method[J].Systems Engineering and Electronics,2011,33(10):2343-2347.(in Chinese)