A Model for Residual Life Prediction Based on Brownian Motion in Framework of Similarity

Residual lifetime RL estimation is a key part in prognostics and health management. This paper addresses the problem of estimating the RL from observed degradation data. A Brownian motion in the framework of a similarity-based model utilizing degradation histories with failure and suspension events is developed to achieve this aim. A novel contribution of this paper is the use of observed degradation data from both failed and suspended historical devices, that is, reference devices, to predict the RL of the operating device. This is performed by comparing the similarity between the operating device and reference devices. An extensive numerical investigation is provided to substantiate the superiority of the proposed model over the classical similarity-based approach. We also fit the model to different groups of simulated data to show the adaptability of the model under a small sample condition. The results show that our developed model can provide better residual life estimation accuracy.