A hybrid approach based on deep neural network and double exponential model for remaining useful life prediction

• Published in: Expert systems with applications Vol. 249; p. 123563
• Main Authors: Liang, Junyuan, Liu, Hui, Xiao, Ning-Cong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-09-2024
• Subjects: Bayesian neural network; Empirical model; Gated Recurrent Unit; Hybrid approaches; Remaining useful life; Uncertainty quantification; Uncertainty quantification; Gated Recurrent Unit; Hybrid approaches; Remaining useful life; Empirical model; Bayesian neural network
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2024.123563

To enhance RUL prediction accuracy and uncertainty quantification, numerous methods have been developed, including model-based, data-driven, and hybrid approaches. However, model-based approaches struggle with complex relationships and uncertainties. Data-driven methods might overlook prior knowledge and struggle with limited data. Hybrid models for RUL prediction face two key challenges: inadequate use of physical information and difficulty in accurately quantifying uncertainty. Aiming at the problems of non-linearity, small sample sizes, and the absence of uncertainty quantification in RUL prediction, this paper introduces a hybrid method aimed at achieving RUL prediction and uncertainty quantification in few-slot scenarios. In this study, a hybrid approach that combines model-based approach and data-driven approach is proposed to achieve accurate RUL prediction. The uncertainty is measured based on a Bayesian framework. Specifically, the proposed method combines the degradation trend model using a double-exponential degradation model (DEDM), and the degradation fluctuations is predicted by a Gated Recurrent Unit (GRU) network to achieve point estimation of the RUL. Subsequently, an ensemble learning method is adopted to integrate the different modules using a Bayesian neural network (BNN) for uncertainty quantification. The applicability and effectiveness of the proposed method is investigated through case studies conducted on the three lithium battery datasets. The comparative analyses are also conducted with commonly used EMD-based approaches. Experimental results demonstrate that the proposed method has good performance in both data leakage and non-leakage scenarios and is more effective than individual methods to achieve accurate RUL prediction under small sample datasets. Finally, this study dedicates to integrate physical models and data-driven models to address the challenge of data drift in future research.