Bayesian更新与EM算法协作下退化数据驱动的剩余寿命估计方法

Degradation Data-Driven Remaining Useful Life Estimation Approach under Collaboration between Bayesian Updating and EM Algorithm

设备的剩余寿命(RUL)估计是对设备进行视情维护、预测与健康管理的关键问题之一.为实现对于单个服役设备退化过程的建模以及RUL的估计,文中提出一种Bayesian更新与期望最大化算法协作下退化数据驱动的RUL估计方法.首先利用指数退化模型来描述设备的退化过程,基于监测的退化数据,利用Bayesian方法对模型的随机参数进行更新,进而得到RUL的概率分布函数和点估计.其次,利用运行设备到当前时刻的监测数据,基于EM算法给出退化模型中非随机未知参数的估计方法,并证明参数迭代估计中每步得到的结果是唯一最优解.最后通过数值仿真和实际数据应用研究,表明文中方法可对单个设备退化过程进行建模,有效估计退化模型中的未知参数,进而得到更好的RUL估计结果.

Remaining useful life （RUL） estimation is one of the key issues in condition-based maintenance and prognostics and health management. To achieve degradation modeling and RUL estimation for the individual equipment in service, a degradation data-driven RUL estimation approach under the collaboration between Bayesian updating and expectation maximization （EM） algorithm is presented. Firstly, an exponential-like degradation model is utilized to describe the equipment degradation processand the stochastic parameters in the model are updated by Bayesian approach. Based on the Bayesian updating results, the probability distribution of the RUL is derived and the point estimation of the RUL is obtained accordingly. Secondly, based on the monitored degradation data to date, a parameter estimation approach for other non-stochastic parameters in the established degradation model is proved. Furthermore, it is proved that the obtained estimation in each iteration is unique and optimal. Finally, a numerical example and a practical case study are provided to show that the presented approach effectively models degradation process for the individual equipment, achieves RUL estimation, estimates the model parameters and generates better results than a previously repo^ted approach in the literature.