Machine Learning Applications for Reliability Engineering: A Review

Mathieu Payette () and Georges Abdul-Nour
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Mathieu Payette: Department of Industrial Engineering, University of Quebec in Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
Georges Abdul-Nour: Department of Industrial Engineering, University of Quebec in Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada

Sustainability, 2023, vol. 15, issue 7, 1-22

Abstract: The treatment of big data as well as the rapid improvement in the speed of data processing are facilitated by the parallelization of computations, cloud computing as well as the increasing number of artificial intelligence techniques. These developments lead to the multiplication of applications and modeling techniques. Reliability engineering includes several research areas such as reliability, availability, maintainability, and safety (RAMS); prognostics and health management (PHM); and asset management (AM), aiming at the realization of the life cycle value. The expansion of artificial intelligence (AI) modeling techniques combined with the various research topics increases the difficulty of practitioners in identifying the appropriate methodologies and techniques applicable. The objective of this publication is to provide an overview of the different machine learning (ML) techniques from the perspective of traditional modeling techniques. Furthermore, it presents a methodology for data science application and how machine learning can be applied in each step. Then, it will demonstrate how ML techniques can be complementary to traditional approaches, and cases from the literature will be presented.

Keywords: artificial intelligence; engineering of asset management; machine learning; prognostic and health management; reliability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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