北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (3): 522-531.doi: 10.13700/j.bh.1001-5965.2015.0175

• 论文 • 上一篇    下一篇

基于退化数据与故障数据的导弹竞争故障预测

丛林虎1, 徐廷学1, 王骞2, 董琪1   

  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
  • 收稿日期:2015-03-26 出版日期:2016-03-20 发布日期:2016-03-25
  • 通讯作者: 徐廷学,Tel.:0535-6635875 E-mail:xtx-yt@163.com E-mail:xtx-yt@163.com
  • 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
  • 基金资助:
    国防预研项目(401080102)

Missile competing fault prediction based on degradation data and fault data

CONG Linhu1, XU Tingxue1, WANG Qian2, DONG Qi1   

  1. 1. Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China;
    2. Unit 71687 of PLA, Xinxiang 453000, China
  • Received:2015-03-26 Online:2016-03-20 Published:2016-03-25
  • Supported by:
    National Defense Pre-Research Foundation of China (401080102)

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。

关键词: 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.

Key words: competing fault, performance degradation, missile, least squares support vector machine (LS-SVM), location-scale model

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