基于随机退化数据建模的设备剩余寿命自适应预测方法

针对现有剩余寿命预测研究中需要多个同类设备历史数据离线估计模型参数的问题,本文提出了一种基于退化数据建模的服役设备剩余寿命自适应预测方法.该方法,利用指数随机退化模型来建模设备的退化过程,基于退化监测数据运用Bayesian方法更新模型的随机参数,进而得到剩余寿命的概率分布函数及点估计.区别于现有方法,本文方法基于设备到当前时刻的监测数据,利用期望最大化算法对模型中的非随机未知参数进行在线估计,由此无需多个同类设备历史数据.最后,通过数值仿真与实例分析,验证了本文方法在剩余寿命预测时的有效性.

A Stochastic Degradation Modeling Based Adaptive Prognostic Approach for Equipment

Current prognostic studies are usually based on historical degradation data, which are collected off line from different devices in a population with the same type.However, such data are not always available in practice.Toward this end, this paper presents a degradation modeling based adaptive remaining useful life prediction method for equipments in service.In the presented method, we use an exponential-like stochastic degradation model to represent the degradation process of equipments.Then, based on the monitored data during the degradation process, Bayesian approach is applied to update the stochastic parameters in the model, so the probability distribution of the predicted remaining useful life is derived as well as its point estimation.Differing from current studies, all unknown non-stochastic parameters in the model are estimated by expectation maximization algorithm, without requiring historical degradation data of multiple devices.Finally, numerical simulations and case study results substantiate the superiority of the presented method in predicting the remaining useful life.