摘要
针对非线性退化设备的剩余寿命预测问题,尚未系统研究考虑测量误差和随机效应的退化建模、先验参数估计及相应的剩余寿命预测方法。首先建立考虑测量误差和随机效应的非线性Wiener退化模型;利用同类设备历史监测数据,基于期望最大化算法估计出退化模型中固定系数和随机系数先验分布;采用状态空间模型描述目标设备当前监测状态,基于Kalman滤波算法迭代估计出随机系数后验分布和当前真实退化状态;利用全概率公式,推导出考虑隐含状态估计不确定性的设备剩余寿命的概率密度函数;仿真实例分析表明,所提方法较现有方法在参数估计误差和剩余寿命预测精度上具有一定优势。
For the problem of remaining life (RL) prediction of the nonlinear degradation device, existing methods have not systematically studied the degradation modeling with measurement error and random effect, the priori parameter estimation, and the corresponding RL prediction method. A nonlinear Wiener degradation model is built considering measurement error and random effect. By using historical condition monitoring (CM) data of similar device, the expectation maximum algorithm is applied to obtain the estimates of the fixed coefficient and the priori distribution of the random coefficients in the degradation model. The state space model is used to describe the current CM state of the target device. The Kalman filter algorithm is applied to iteratively obtain the posterior distribution of the random coefficients and the current real degradation state. The full -probability formula is used to deduce the probability density function of the RL considering the estimation uncertainty of the implicit state. The simulation example analysis shows that this method has advantages over the existing methods in parameter estimation error and RL prediction accuracy.
作者
蔡忠义
陈云翔
郭建胜
王泽洲
邓林
CAI Zhongyi;CHEN Yunxiang;GUO Jiansheng;WANG Zezhou;Deng Lin(Equipment Management & UAV Engineering College, Air Force Engineering University, Xi’an 710051, China;The 29th Research Institute, China Electronics Technology Group Corporation, Chengdu 610036, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2019年第7期1658-1664,共7页
Systems Engineering and Electronics
基金
国家自然科学基金(71601183)
中国博士后科学基金(2017M623415)
国家国防科工局技术基础项目(JSZL2016210B001)资助课题
关键词
剩余寿命预测
非线性退化模型
测量误差
随机效应
remaining life (RL) prediction
nonlinear degradation model
measurement error
random effect
