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

Shahil Kumar, Krish Kumar Raj, Maurizio Cirrincione, Giansalvo Cirrincione, Vincenzo Franzitta () and Rahul Ranjeev Kumar
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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

Energies, 2024, vol. 17, issue 22, 1-46

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.

Keywords: bearings; condition monitoring; fault diagnosis; gearbox; health indicators; misalignment; prognosis; rotating machines; remaining useful life (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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