A Remaining Useful Life Prediction Method in the Early Stage of Stochastic Degradation Process

In order to achieve more accurate predicted RUL in the early stage of degradation, a novel remaining useful life (RUL) prediction method for the stochastic degradation process is proposed. Technically, modeling the degradation process as a Wiener process (WP) whose drift increment is a weighted sum of kernel functions can flexibly depict the nonlinear degradation trend. Introducing a long short term memory (LSTM) network can capture the long-term dependencies of the offline experimental and online observed degradation data to forecast the future degradation increment. Then, based on the degradation model, a numerical approximate distribution of the RUL is derived to quantify the uncertainty of the predicted RUL. Finally, a practical case study of lithium-ion batteries is provided to demonstrate the high accuracy of the proposed methods for RUL prediction especially in the early stage of degradation.

Automated summary

A novel method for predicting remaining useful life (RUL) in the early stage of degradation is proposed, which uses Wiener process and LSTM network to model degradation process, predict RUL, and quantify uncertainty. The high accuracy of the proposed methods is demonstrated in a practical case study of lithium-ion batteries.