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

被引:0
|
作者
Kumar, Shahil [1 ]
Raj, Krish Kumar [1 ]
Cirrincione, Maurizio [1 ]
Cirrincione, Giansalvo [2 ]
Franzitta, Vincenzo [3 ]
Kumar, Rahul Ranjeev [1 ]
机构
[1] Univ South Pacific, Sch Informat Technol Engn Math & Phys, Private Mail Bag Laucala Campus, Suva, Fiji
[2] Univ Picardie Jules Verne, Lab Novel Technol, F-80000 Amiens, France
[3] Univ Palermo, Dept Engn, I-90128 Palermo, Italy
关键词
bearings; condition monitoring; fault diagnosis; gearbox; health indicators; misalignment; prognosis; rotating machines; remaining useful life; ROLLING ELEMENT BEARINGS; FAULT-DIAGNOSIS; NEURAL-NETWORKS; MAHALANOBIS DISTANCE; INDUCTION-MOTORS; PREDICTION; PROGNOSTICS; FRAMEWORK; SYSTEMS; OPTIMIZATION;
D O I
10.3390/en17225538
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:46
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