电子学报 ›› 2021, Vol. 49 ›› Issue (3): 500-509.DOI: 10.12263/DZXB.20200050

• 学术论文 • 上一篇    下一篇

基于EM-EKF与隐含比例退化模型的机载电子设备剩余寿命自适应预测

陈云翔, 王泽洲, 蔡忠义, 项华春, 王莉莉   

  1. 空军工程大学装备管理与无人机工程学院, 陕西西安 710051
  • 收稿日期:2020-01-02 修回日期:2020-03-06 出版日期:2021-03-25 发布日期:2021-03-25
  • 通讯作者: 蔡忠义
  • 作者简介:陈云翔 男,1962年10月出生,江苏句容人,现为空军工程大学装备管理与无人机工程学院教授、博导,主要研究方向为装备可靠性评估、装备维修保障.E-mail:jing326952@163.com;王泽洲 男,1992年4月出生,山西长治人,现为空军工程大学装备管理与无人机工程学院博士研究生,主要研究方向为装备可靠性评估、剩余寿命预测.E-mail:350276267@qq.com;项华春 男,1980年3月出生,浙江龙游人,现为空军工程大学装备管理与无人机工程学院副教授、硕导,主要研究方向为装备可靠性评估、装备维修保障.E-mail:xhc09260926@163.com;王莉莉 女,1983年6月出生,江苏南京人,现为空军工程大学装备管理与无人机工程学院副教授、硕导,主要研究方向为装备维修保障、作战效能评估研究.E-mail:8574886@qq.com

Adaptive Prediction of Remaining Useful Lifetime for the Airborne Electronic Equipment Based on the EM-EKF Algorithm and Hidden Degradation Model with the Proportion Relationship

CHENG Yun-xiang, WANG Ze-zhou, CAI Zhong-yi, XIANG Hua-chun, WANAG Li-li   

  1. Equipment Management&UAV Engineering College, Air Force Engineering University, Xi'an, Shaanxi 710051, China
  • Received:2020-01-02 Revised:2020-03-06 Online:2021-03-25 Published:2021-03-25

摘要: 针对现有机载电子设备剩余寿命自适应预测方法在新研小样本条件下,未能综合考虑设备隐含退化建模与漂移系数在线更新的问题,本文提出一种基于期望最大-扩展卡尔曼滤波(Expectation Maximization-Extended Kalman Filter,EM-EKF)与隐含比例退化模型的机载电子设备剩余寿命自适应预测方法.首先,基于非线性Wiener过程构建带比例关系的设备隐含退化模型;其次,在引入漂移系数更新机制的基础上建立设备退化状态方程,并采用EKF算法同步更新设备退化状态与漂移系数;然后,采用EM-EKF算法实现对退化模型参数的自适应估计;最后,基于全概率公式,推导出设备剩余寿命的概率密度函数.通过对单台微机械陀螺仪实测数据进行分析,验证了本文所提方法具有更好的模型拟合性与预测准确性.

关键词: 剩余寿命预测, Wiener过程, 隐含退化模型, 比例关系, EM-EKF算法

Abstract: Aiming at the problem that the existing adaptive prediction methods of remaining useful lifetime (RUL) for the airborne electronic equipment fail to comprehensively consider the hidden degradation modeling and online drift coefficients updating in the condition of newly researched and small sample,an adaptive prediction method for the airborne electronic equipment’s RUL based on the EM-EKF algorithm and hidden degradation model with proportion relationship is proposed.Firstly,based on the nonlinear Wiener process,a hidden degradation model with the proportion relationship is constructed.Next,the degradation state equation of the equipment is established based on the drift coefficient update mechanism,and the EKF algorithm is used to update the degradation status and drift coefficient.And then,the EM-EKF algorithm is used to adaptively estimate the parameters of the degradation model.Finally,based on the full probability formula,the probability density function (PDF) of RUL is derived.By analyzing the measured data of a single micromechanical gyroscope,it is verified that the proposed method has better model fitting and prediction accuracy.

Key words: remaining useful lifetime prediction, Wiener process, hidden degradation model, proportion relationship, EM-EKF algorithm

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