基于退化数据与故障数据的导弹竞争故障预测

丛林虎; 徐廷学; 王骞; 董琪

doi:10.13700/j.bh.1001-5965.2015.0175

基于退化数据与故障数据的导弹竞争故障预测

doi: 10.13700/j.bh.1001-5965.2015.0175

丛林虎¹,
徐廷学^1, ,,
王骞²,
董琪¹

1.
海军航空工程学院兵器科学与技术系, 烟台 264001
2. 71687部队, 新乡 453000

基金项目: 国防预研项目(401080102)

详细信息

作者简介:
丛林虎男,博士研究生。主要研究方向:装备综合保障。Tel.:0535-6635061 E-mail:342743812@qq.com;徐廷学男,博士,教授,博士生导师。主要研究方向:装备综合保障。Tel.:0535-6635875 E-mail:xtx-yt@163.com

通讯作者:
徐廷学,Tel.:0535-6635875 E-mail:xtx-yt@163.com

中图分类号: TJ760
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出版历程
- 收稿日期: 2015-03-26
- 网络出版日期: 2016-03-20

Missile competing fault prediction based on degradation data and fault data

1.
Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China
2. Unit 71687 of PLA, Xinxiang 453000, China

Funds: National Defense Pre-Research Foundation of China (401080102)

摘要

摘要: 针对具有多元退化量的导弹竞争故障预测问题,分析了导弹退化特性,并在考虑突发故障与退化故障相关性的基础上,建立了具有多元退化量的导弹竞争故障预测模型。对导弹性能退化数据与突发故障数据进行统计推断,确定了数据的分布类型,在此基础上对竞争故障预测模型的参数进行了求解。针对导弹性能退化数据分布参数存在非线性、小样本等问题,运用最小二乘支持向量机(LS-SVM)预测模型对性能退化数据的分布参数进行了预测,得到了性能退化数据未来某一时刻的分布函数;针对退化量与突发故障的相关性,应用位置-尺度模型分析了退化量与突发故障的关系,得出了突发故障与退化量的相关参数,进而根据导弹竞争故障预测模型得到了导弹未来一段时间内的竞争故障概率。以贮存状态下的整批导弹为例,实现了导弹竞争故障预测,并与其他预测方法进行了对比,结果验证了方法的合理性与有效性。
- 竞争故障 /
- 性能退化 /
- 导弹 /
- 最小二乘支持向量机(LS-SVM) /
- 位置-尺度模型
Abstract: Aiming at the problem of missile competing fault prediction which has multivariate degradation data, the characteristics of missile degradation are analyzed and missile competing fault prediction model which has multivariate degradation data is established considering the correlation between sudden fault and degradation fault. The distribution patterns of performance degradation data and sudden fault data are determined through statistical inference, and on this basis the parameters for competing fault prediction model are solved. Aiming at the distribution parameters of performance degradation data having the feature of small sample and nonlinearity, least square support vector machine (LS-SVM) prediction algorithm is used to predict the distribution parameters of performance degradation data in order to get the future distribution function. Aiming at the correlation between sudden fault and degradation data, the correlation parameters of sudden fault and degradation data are obtained using location-scale model to analyze the relations between sudden fault and degradation data. Furthermore, the future missile competing fault probability can be obtained according to the missile competing fault prediction model. The efficiency and validity of missile competing fault prediction model are verified by case analysis and contrasting with other prediction methods.
- competing fault /
- performance degradation /
- missile /
- least squares support vector machine (LS-SVM) /
- location-scale model

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参考文献(21)

[1]	赵建印. 基于性能退化数据的可靠性建模与应用研究[D].长沙:国防科学技术大学,2005:61-64. ZHAO J Y.Reliability modeling and application study based on the data of performance degradation[D].Changsha:National University of Defense Technology,2005:61-64(in Chinese).
[2]	HUANG W, DIETRICH D L.An alternative degradation reliability modeling approach using maximum likelihood estimation[J].IEEE Transactions on Reliability,2005,54(2):310-317.
[3]	HUANG W, ASKIN R G.Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation[J].Quality and Reliability Engineering International,2003,19(3):241-254.
[4]	BOCCHETTI D, GIORGIO M,GUIDA M,et al.A competing risk model for the reliability of cylinder liners in marine diesel engines[J].Reliability Engineering and System Safety,2009,94(8):1299-1307.
[5]	BEDFORD T. Competing risk modeling in reliability[M]//Modern statistical and mathematical methods in reliability.New Jersey:Word Scientific Publisher,2006:23-40.
[6]	LEHMANN A. Joint modeling of degradation and failure time data[J].Journal of Statistical Planning and Inference,2009,139(5):1693-1706.
[7]	LI W, PHAM H.Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks[J].IEEE Transactions on Reliablity,2005,54(2):297-303.
[8]	BAGDONAVICIUS V, BIKELIS A,KAZAKEVICIUS V,et al.Analysis of joint multiple failure mode and linear degradation data with renewals[J].Journal of Statistical Planning and Inference,2007,137(7):2191-2207.
[9]	赵建印,刘芳, 奚文俊.退化失效与突发失效共存下产品可靠性模型与评估方法研究[J].兵工学报,2011,32(9):1136-1139. ZHAO J Y,LIU F, XI W J.Reliability model and evaluation method of products in competing failure modes[J].Acta Armamentarii,2011,32(9):1136-1139(in Chinese).
[10]	苏春,张恒. 基于性能退化数据和竞争失效分析的可靠性评估[J].机械强度,2011,33(2):196-200. SU C,ZHANG H.Reliability assessment based on performance degradation data and competiong failure analysis[J].Journal of Mechanical Strength,2011,33(2):196-200(in Chinese).
[11]	王华伟,高军, 吴海桥.基于竞争失效的航空发动机剩余寿命预测[J].机械工程学报,2014,50(6):197-205. WANG H W,GAO J,WU H Q.Residual remaining life prediction based on competing failures for aircraft engines[J].Journal of Mechanical Engineering,2014,50(6):197-205(in Chinese).
[12]	吴翊,李永乐, 胡庆军.应用数理统计[M].北京:国防科技大学出版社,2008:112-120. WU Y,LI Y L,HU Q J.Application mathematical statistic[M].Beijing:National University of Defence Technology Press,2008:112-120(in Chinese).
[13]	丛林虎,徐廷学, 杨继坤,等.导弹退化故障预测方法研究[J].电光与控制,2014,21(5):78-82. CONG L H,XU T X,YANG J K,et al.A method for missile degradation fault prediction[J].Electronics Optics & Control,2014,21(5):78-82(in Chinese).
[14]	金良琼. 两参数Weibull分布的参数估计[D].昆明:云南大学,2010:6-16. JIN L Q.Two parameter estimation for Weibull distribution[D].Kunming:Yunnan University,2010:6-16(in Chinese).
[15]	张广明,袁宇浩, 龚松建.基于改进最小二乘支持向量机方法的短期风速预测[J].上海交通大学学报,2011,45(8):1125-1129. ZHANG G M,YUAN Y H,GONG J S.A predictive model of short-term wind speed based on improved least squares support vector machine algorithm[J].Journal of Shanghai Jiao Tong University, 2011,45(8):1125-1129(in Chinese).
[16]	尉询楷,李应红, 张朴,等.基于支持向量机的时间序列预测模型分析与应用[J].系统工程与电子技术,2005,27(3):529-532. WEI X K,LI Y H,ZHANG P,et al.Analysis and applications of time series forecasting model via support vector machines[J].Systems Engineering and Electronics,2005,27(3):529-532(in Chinese).
[17]	洪杰,韩磊,苗学问, 等.基于支持向量机的滚动轴承状态寿命评估[J].北京航空航天大学学报,2010,36(8):896-899. HONG J,HAN L,MIAO X W,et al.Assessment based on support vector machine for rolling bearing grade-life[J].Journal of Beijing University of Aeronautics and Astronautics,2010,36(8):896-899(in Chinese).
[18]	唐杰明,刘俊勇,杨可,等. 基于灰色模型和最小二乘支持向量机的电力短期负荷组合预测[J].电网技术,2009,33(3):63-68. TANG J M,LIU J Y,YANG K,et al.Short-term load combination forecasting by grey model and least square support vector machine[J].Power System Technology,2009,33(3):63-68(in Chinese).
[19]	LAWLESS J F. 寿命数据中的统计模型与方法[M].茆诗松,译.北京:中国统计出版社,1998:40-50. LAWLESS J F.Statistical models and methods for lifetime data[M].MAO S S,translated.Beijing:China Statical Publishing House,1998:40-50(in Chinese).
[20]	温艳清,刘保亮. 完全数据下Weibull分布参数的极大似然估计[J].应用数学,2008,21(增刊):67-70. WEN Y Q,LIU B L.Complete data parameters estimation under Weibull distribution[J].Mathematica Applicata,2008,21(S):67-70(in Chinese).
[21]	姚云峰,伍逸夫, 冯玉光.装备健康状态评估方法研究[J].现代防御技术,2012,40(5):156-161. YAO Y F,WU Y F,FENG Y G.Health condition assessment of equipment[J].Modern Defence Technology,2012,40(5):156-161(in Chinese).