Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction

Zhanhang Li, Jian Zhou, Hani Nassif, David Coit and Jinwoo Bae

Reliability Engineering and System Safety, 2023, vol. 232, issue C

Abstract: Many methods have been developed for degradation prediction. Machine learning-based methods have the advantages of capturing complex non-linear relations in degradation processes, but they often suffer from limitations of available dataset. Physics-based methods are closely related to physical degradation mechanism, while they are often limited by the incompleteness of modeling. This work presents a new hybrid method for degradation prediction with bias correction by combining the degradation tendency information from the physics-based stochastic degradation model with machine learning approaches. A gamma-gamma two-stage degradation model is adopted to obtain the expected degradation path, which is used to develop the input of Bi-direction long-short term memory (Bi-LSTM) network for degradation prediction. Case studies of bridge deck rebar degradation are conducted to demonstrate the applicability of the proposed approach, where actual data is collected based on 33-months of rebar degradation experiments under different environmental conditions. The results show that the proposed hybrid method outperforms other machine learning-based methods. Specifically, the mean square error of rebar degradation prediction is reduced at least by 27% under the proposed approach in comparison with pure Bi-LSTM. This work provides insights for performing bias learning in prognosis by leveraging the advantages of physics-based methods and machine learning approaches.

Keywords: Hybrid model; Bias learning; Degradation prediction; Neural networks (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006937

DOI: 10.1016/j.ress.2022.109078

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