Personalized Federated Transfer Learning for Cycle-Life Prediction of Lithium-Ion Batteries in Heterogeneous Clients With Data Privacy Protection

Huang, Cheng-Geng; Li, He; Peng, Weiwen; Tang, Loon Ching; Ye, Zhi-Sheng

doi:10.1109/jiot.2024.3433460

IEEE Internet Things J.

2024

DOI: 10.1109/jiot.2024.3433460

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Personalized Federated Transfer Learning for Cycle-Life Prediction of Lithium-Ion Batteries in Heterogeneous Clients With Data Privacy Protection

Cheng-Geng Huang,

He Li,

Weiwen Peng

et al.

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A Parallel Prognostic Method Integrating Uncertainty Quantification for Probabilistic Remaining Useful Life Prediction of Aero-Engine

Wang,

Zhang,

et al. 2024

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Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount of monitoring data is recorded. Traditional methods, such as machine-learning-based methods and statistical-data-driven methods, are ineffective in matching when faced with big data thus leading to poor predictions. As a result, deep-learning-based methods are extensively utilized due to their efficient capability to excavate deep features and realize accurate predictions. However, most deep-learning-based methods only provide point estimations and ignore the prediction uncertainty. To address this limitation, this paper proposes a parallel prognostic network to sufficiently excavate the degradation features from multiple dimensions for more accurate RUL prediction. In addition, accurate calculation of model evidence is extremely difficult when dealing with big data so the Monte Carlo dropout is employed to infer the model weights under low computational cost and high scalability to obtain a probabilistic RUL prediction. Finally, the C-MAPSS aero-engine dataset is employed to validate the proposed dual-channel framework. The experimental results illustrate its superior prediction performance compared to other deep learning methods and the ability to quantify prediction uncertainty.

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A Parallel Prognostic Method Integrating Uncertainty Quantification for Probabilistic Remaining Useful Life Prediction of Aero-Engine

Wang,

Zhang,

et al. 2024

Processes

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show abstract

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Personalized Federated Transfer Learning for Cycle-Life Prediction of Lithium-Ion Batteries in Heterogeneous Clients With Data Privacy Protection

Cited by 1 publication

References 33 publications

A Parallel Prognostic Method Integrating Uncertainty Quantification for Probabilistic Remaining Useful Life Prediction of Aero-Engine

A Parallel Prognostic Method Integrating Uncertainty Quantification for Probabilistic Remaining Useful Life Prediction of Aero-Engine

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