A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines

Nguyen Khanh T. P.; Medjaher Kamal; Tran Do T.

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A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines

机译：A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines

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The past decade has witnessed the adoption of artificial intelligence (AI) in various applications. It is of no exception in the area of prognostics and health management (PHM) where the capacity of AI has been highlighted through numerous studies. In this paper, we present a comprehensive review of AI-based solutions in engineering PHM. This review serves as a guideline for researchers and practitioners with varying levels of experience seeking to broaden their know-how about AI-based PHM. Specifically, we provide both a broad quantitative analysis and a comprehensive qualitative examination of the roles of AI in PHM. The quantitative analysis offers an insight into the research community's interest in AI-based approaches, focusing on the evolution of research trends and their developments in different PHM application areas. The qualitative survey gives a complete picture on the employment of AI in each stage of the PHM process, from data preparation to decision support. Based on the strengths and weaknesses of existing methods, we derive a general guideline for choosing proper techniques for each specific PHM task, aiming to level up maintenance practitioners' efficiency in implementing PHM solutions. Finally, the review discusses challenges and future research directions in the development of autonomous intelligent PHM solutions.

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《Artificial Intelligence Review: An International Science and Engineering Journal》 |2023年第4期|3659-3709|共51页
作者
Nguyen Khanh T. P.; Medjaher Kamal; Tran Do T.;
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作者单位

Toulouse INP;

Tetra Pak;

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原文格式 PDF
正文语种英语
中图分类人工智能理论;
关键词
Artificial intelligence; Prognostics and health management; Condition monitoring; Machine learning; Predictive maintenance; Decision-making support; REMAINING USEFUL LIFE; BEARING FAULT-DETECTION; FISHER DISCRIMINANT-ANALYSIS; RECURRENT NEURAL-NETWORK; OF-THE-ART; ROTATING MACHINERY; RESIDUAL-LIFE; GENETIC ALGORITHMS; FEATURE-SELECTION; DIAGNOSIS METHOD;

A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines

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