Prognosis of degradation based on a new dynamic method for remaining useful life prediction

Abstract

Purpose The purpose of this paper is to create a new method of prognosis based on remaining useful life (RUL) prediction for degradation assessment. Design/methodology/approach In the present paper the authors describe a new method of prognosis to improve the accuracy of forecasting the system state. This framework of forecasting integrates the model-based information and the hybrid approach, which employs the structured residuals in the first part and the particle filter in the second part. Findings The performance of the suggested fusion framework is employed to predict the RUL of battery pack in hybrid electric vehicle. The results show that the proposed method is plausible due to the good prediction of RUL, and can be effectively applied to many systems for prognosis. Originality/value In this study the authors illustrate how the suggested method can provide an accurate prediction of the RUL over conventional data-driven methods without physical model and classical particle filter with a single damage model.