具有不确定测量的非线性随机退化系统剩余寿命预测  

Remaining Useful Life Prediction of Nonlinear Stochastic Degrading Systems Subject to Uncertain Measurements

作　　者：司小胜[1] 胡昌华[1] 李娟[2] 孙国玺[3] 张琪[1] 

机构地区：[1]第二炮兵工程大学控制工程系,西安710025 [2]青岛农业大学机电工程学院,青岛266106 [3]广东石油化工学院广东省石化装备故障诊断重点实验室,广东茂名525000

出　　处：《上海交通大学学报》2015年第6期855-860,867,共7页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金(61174030;61374126;61473094)项目资助

摘　　要：提出了一类同时考虑不确定测量和非线性随机退化的退化建模方法,通过Kalman滤波技术对受不确定测量影响的潜在退化状态进行实时估计;基于此,通过首达时间的概念得到了同时考虑退化非线性特征、退化状态不确定性及测量不确定性的剩余寿命分布;此外,提出了一种基于极大似然方法的退化模型参数估计方法,并通过陀螺仪的退化测量数据验证了所提方法可以提高剩余寿命估计的准确性.A class of degrada oration and uncertain measu ring technique was utilized t tion modeling approach was proposed, in which the nonlinear stochastic deteri- rements of the system were considered simultaneously, and the Kalman filte- o estimate the underlying degradation state. Based on the estimated degrada- tion state, the analytical RUL distribution was derived according to the concept of the first passage time which accounted for the uncertainties in the estimated degradation state and measurements, and the effect of the degradation nonlinearity. Additionally, a parameter estimation method for the developed model was presented based on the maximum likelihood method. Finally, a case study of the gyros verified that the proposed method could improve the accuracy of the predicted RUL.

关 键 词：预测与健康管理 寿命预测 退化建模 KALMAN滤波 不确定测量 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]