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

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  • Shahil Kumar

    (School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Private Mail Bag Laucala Campus, Suva 1168, Fiji)

  • Krish Kumar Raj

    (School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Private Mail Bag Laucala Campus, Suva 1168, Fiji)

  • Maurizio Cirrincione

    (School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Private Mail Bag Laucala Campus, Suva 1168, Fiji)

  • Giansalvo Cirrincione

    (Laboratory of Novel Technologies, University of Picardie Jules Verne, 80000 Amiens, France)

  • Vincenzo Franzitta

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Rahul Ranjeev Kumar

    (School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Private Mail Bag Laucala Campus, Suva 1168, Fiji)

Abstract

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.

Suggested Citation

  • Shahil Kumar & Krish Kumar Raj & Maurizio Cirrincione & Giansalvo Cirrincione & Vincenzo Franzitta & Rahul Ranjeev Kumar, 2024. "A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery," Energies, MDPI, vol. 17(22), pages 1-46, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5538-:d:1514947
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    References listed on IDEAS

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    1. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    2. Shi, Yue & Zhu, Weihang & Xiang, Yisha & Feng, Qianmei, 2020. "Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
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    5. Jin Cui & Licai Cao & Tianxiao Zhang, 2024. "A two-stage Gaussian process regression model for remaining useful prediction of bearings," Journal of Risk and Reliability, , vol. 238(2), pages 333-348, April.
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    8. Fan, Yuantao & Nowaczyk, Sławomir & Rögnvaldsson, Thorsteinn, 2020. "Transfer learning for remaining useful life prediction based on consensus self-organizing models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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