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A hybrid prognostic method for system degradation based on particle filter and relevance vector machine

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  • Chang, Yang
  • Fang, Huajing

Abstract

Prognostics of the remaining useful life has become a critical technique to ensure the reliability and safety of system, however, due to the uncertainty of system degradation, the prognostic result is usually not so satisfactory. To solve this problem, a hybrid prognostic scheme with the capability of uncertainty assessment is proposed in this paper, which combines particle filter (PF) and relevance vector machine (RVM). The prognostic result comprises a set of deterministic prediction values to represent the degradation process and a prediction interval to evaluate the prediction uncertainty. In order to examine the performance of the proposed hybrid method, four types of comparative experiments based on two types of lithium-ion battery datasets and two degradation models are performed. The experimental results show that the proposed hybrid scheme is a reliable prognostic method which can ensure the accuracy of the deterministic prediction result and provide precise assessment for the prediction uncertainty.

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  • Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    • Handle: RePEc:eee:reensy:v:186:y:2019:i:c:p:51-63
    DOI: 10.1016/j.ress.2019.02.011
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    8. Shao-Xun Liu & Ya-Fu Zhou & Yan-Liang Liu & Jing Lian & Li-Jian Huang, 2021. "A Method for Battery Health Estimation Based on Charging Time Segment," Energies, MDPI, vol. 14(9), pages 1-15, May.
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    10. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Lei, Zhenzhen & Zhang, Yuanjian, 2023. "State of health estimation for lithium-ion batteries based on hybrid attention and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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    13. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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    15. Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, İ. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Chuang Sun & An Qu & Jun Zhang & Qiyang Shi & Zhenhong Jia, 2022. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Variational Mode Decomposition and Machine Learning Algorithm," Energies, MDPI, vol. 16(1), pages 1-15, December.
    17. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    18. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. Bo Pang & Li Chen & Zuomin Dong, 2022. "Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries," Energies, MDPI, vol. 15(15), pages 1-12, August.
    20. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    21. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    22. Lin, Yan-Hui & Jiao, Xin-Lei, 2021. "Adaptive Kernel Auxiliary Particle Filter Method for Degradation State Estimation," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    23. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).

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