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.