带测量误差的非线性退化过程建模与剩余寿命估计

Nonlinear Degradation Process Modeling and Remaining Useful Life Estimation Subject to Measurement Error

剩余寿命(Remaining useful lifetime,RUL)估计是设备视情维护和预测与健康管理(Prognostics and health management,PHM)中的一项关键问题.采用退化过程建模进行剩余寿命估计的研究中,现有方法仅考虑了具有线性或可以线性化的退化轨迹的问题.本文提出了一种基于扩散过程的非线性退化过程建模方法,在首达时间的意义下,推导出了剩余寿命的分布.该方法可以描述一般的非线性退化轨迹,现有的线性退化建模方法是其特例.在参数的推断中,考虑到真实的退化过程受到测量误差的影响,难以直接测量得到,因此,在退化建模的过程中引入了测量误差对退化观测数据的影响,通过观测数据,提出了一种退化模型未知参数的极大似然估计方法.最后,通过激光发生器和陀螺仪的退化测量数据验证了本文方法明显优于线性建模方法,具有潜在的工程应用价值.

Remaining useful lifetime（RUL） estimation is a key issue in condition-based maintenance and prognostics and health management.In the literature about the RUL estimation based on degradation process modeling,the current methods only consider the cases in which the degradation path is linear or can be readily linearized.In this paper,a nonlinear degradation process modeling approach is proposed based on diffusion process and the RUL distribution is derived in the sense of the first passage time.It is observed that the proposed method can be used to describe a general nonlinear degradation path and the current linear approaches turn out to be its special cases.As for parameter inference,considering the fact that the true degradation cannot be observed directly due to measurement error,the measurement error is introduced into degradation modeling.Through the degradation measurement data,a maximum likelihood estimation method is presented to estimate the unknown parameters of the degradation model.Finally,we demonstrate the usefulness of the proposed model via laser data and gyro s drifting data.The results show that the proposed method can generate reasonably better results than the linear model and thus can be potentially applied in practice.