Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

Wang, Ya-Xiong; Zhao, Shangyu; Wang, Shiquan; Ou, Kai; Zhang, Jiujun

doi:10.1016/j.egyai.2024.100405

Energy and AI

2024

DOI: 10.1016/j.egyai.2024.100405

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Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

Ya-Xiong Wang,

Shangyu Zhao,

Shiquan Wang

et al.

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A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

Kumar,

Raj,

Cirrincione

et al. 2024

Energies

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This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.

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A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

Kumar,

Raj,

Cirrincione

et al. 2024

Energies

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show abstract

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Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

Cited by 1 publication

References 42 publications

A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

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