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基于多机理竞争退化的导弹贮存可靠性分析
引用本文:罗湘勇,黄小凯. 基于多机理竞争退化的导弹贮存可靠性分析[J]. 北京航空航天大学学报, 2013, 39(5): 701-705
作者姓名:罗湘勇  黄小凯
作者单位:北京航空航天大学可靠性与系统工程学院,北京,100191;北京航空航天大学可靠性与系统工程学院,北京,100191
摘    要:
远程转换开关、无线电高度表、雷达角自动装置、陀螺仪和弹上电源是影响导弹长期贮存可靠性的关键部件.明确导弹贮存剖面,通过定期检测得到长期贮存条件下各关键部件的特征电压值,然后采用移动标准偏差方法描述了各特征电压值的贮存稳定性水平,并对其进行退化规律拟合求得各关键部件贮存寿命.提炼出10套导弹产品的各关键部件贮存寿命,通过分布假设检验和分布参数辨识,求得各关键部件的贮存可靠度模型,最后根据导弹产品随机性、分散性、多机理竞争特点,分析导弹产品在悲观和乐观情况下的可靠性结果.本文的研究思路和结论能为长期贮存条件下贮存类产品的贮存可靠性评估、贮存维护方案研究提供技术支撑.

关 键 词:导弹  随机性  分散性  多机理竞争退化  贮存可靠性
收稿时间:2012-09-06

Storage reliability analysis of missile based on multi-mechanism competition degradation method
Luo Xiangyong Huang Xiaokai. Storage reliability analysis of missile based on multi-mechanism competition degradation method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(5): 701-705
Authors:Luo Xiangyong Huang Xiaokai
Affiliation:School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
Abstract:
Remote switch, radio altimeter, radar angle automatic device, gyroscope and power equipment are the main influence equipments for long-term storage reliability of missile. Firstly, the storage profile of missile was defined, and feature voltage value of influence equipments were obtained from regular tests in the long-term storage condition. Secondly, the storage stability levels of the feature voltage value were considered by the moving standard deviation method, and the storage lives of all main influence equipments were obtained from fitting for storage stability levels. Thirdly, storage lives of main influence equipments of 10 missiles were obtained, and the distributions and their parameters were identified. Finally, the multi-mechanism competition results of missile storage reliability were obtained by the multi-mechanism competition degradation method according to different consideration of missile storage lives. The thought method and conclusions of the paper have significant meaning for the improving of missile storage reliability prediction and design.
Keywords:
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