Lebesgue Sampling Based Deep Belief Network for Lithium-Ion Battery Diagnosis and Prognosis

Abstract

Fault diagnosis and prognosis (FDP) is critical for ensuring system reliability and reducing operation and maintenance (O&M) costs. Lebesgue sampling based FDP (LS-FDP) is an event-based approach with the advantages of cost-efficiency, uncertainty management, and less computation. In previous works, LS-FDP approaches are mainly model-based. However, fault dynamic modeling is difficult and time consuming for some complex systems and this severely hinders the applications of LS-FDP. To address this problem, this article presents a data-driven based LS-FDP framework in which deep belief networks (DBN) and particle filter (PF) are integrated to achieve fault state estimation and remaining useful life prediction. In the proposed approach, DBN learns the state evolution model and the Lebesgue time transition model, which are used as diagnostic and prognostic models in PF for FDP. A series of offline and online experiments are conducted on lithium-ion batteries to verify the proposed method. Experimental results and comparison studies show that the proposed approach has higher efficiency in terms of computation and better performance in terms of FDP accuracy and precision.