系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (10): 3275-3281.doi: 10.12305/j.issn.1001-506X.2022.10.34

• 可靠性 • 上一篇    下一篇

基于改进PCA的导弹装备健康表征参数提取方法

郭璐1,2,*, 刘晓东1, 魏东涛3, 朱璞1   

  1. 1. 空军工程大学装备管理与无人机工程学院, 陕西 西安 710051
    2. 江南机电设计研究所, 贵州 贵阳 550009
    3. 空军勤务学院, 江苏 徐州 221000
  • 收稿日期:2021-09-28 出版日期:2022-09-20 发布日期:2022-10-24
  • 通讯作者: 郭璐
  • 作者简介:郭璐(1991—), 女, 高级工程师, 博士研究生, 主要研究方向为防空装备综合保障总体设计|刘晓东(1966—), 男, 教授, 博士, 主要研究方向为军事装备学|魏东涛(1985—), 男, 讲师, 博士研究生, 主要研究方向为军事装备学|朱璞(1993—), 男, 硕士研究生, 主要研究方向为军事装备保障
  • 基金资助:
    国防科工局重大基础科研项目(JCKY2017204A011)

Extraction method of missile equipment health characterization parameters based on improved PCA

Lu GUO1,2,*, Xiaodong LIU1, Dongtao WEI3, Pu ZHU1   

  1. 1. College of Equipment Management and UAV Engineering, Air Force Engineering University, Xi'an 710051, China
    2. Jiangnan Mechanical and Electrical Design Institute, Guiyang 550009, China
    3. Air Force Logistics College, Xuzhou 221000, China
  • Received:2021-09-28 Online:2022-09-20 Published:2022-10-24
  • Contact: Lu GUO

摘要:

针对导弹装备健康状态信息复杂且相互交融、健康表征参数难以提取的问题, 提出一种基于改进主成分分析(principal component analysis, PCA)的装备健康状态低维敏感表征参数的确定方法。该方法先开展装备扩展故障模式及影响分析, 构建初始高维特征参数集, 再利用改进PCA对参数集进行降维处理, 在最大化高维表征参数全局特征方差的目标下, 提取出非线性表征参数子集。将该方法应用到导弹舵机健康评估实验中, 使用故障注入模拟设备进行验证。结果表明, 采用所提方法提取的健康表征参数对舵机健康状态识别准确率高, 说明所提方法在提取导弹装备健康表征参数中具有明显的优越性。

关键词: 导弹装备, 健康管理, 表征参数, 改进主成分分析, 舵机

Abstract:

Aiming at the problems of complex and integrated missile equipment health status information and difficult to extract health characterization parameters, a method for determining low-dimensional sensitive characterization parameters of equipment health status based on improved principal component analysis (PCA) is proposed. Firstly, the equipment extensional failure mode and effect analysis are carried out to construct the initial high-dimensional characteristic parameter set. Then, the improved PCA is used to reduce the dimension of the parameter set. Under the goal of maximizing the global characteristic variance of the high-dimensional characterization parameters, the nonlinear characterization parameters subset is extracted. The proposed method is applied to the health evaluation experiment of missile steering gear, and verified by the fault injection simulation equipment. The results show that the health characterization parameters extracted by the proposed method have high recognition accuracy for the health status of steering gear, indicating that the proposed method has obvious advantages in the extraction of missile equipment health characterization parameters.

Key words: missile equipment, health management, characterization parameter, improved principal component analysis (PCA), steering gear

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