Asset management, reliability and prognostics modeling techniques

Payette, M. et Abdul-Nour, G. (2023). Asset management, reliability and prognostics modeling techniques. Sustainability, 15 (9). Article 7493. ISSN 2071-1050 DOI 10.3390/su15097493

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Résumé

Abstract
In recent years, reliability engineering has seen significant growth in data-driven modeling, mainly due to the democratization of sensing technologies, big data processing, and computing capabilities. It has also seen a paradigm shift, with Engineering of Asset Management (EAM) becoming widely accepted as a high-level framework to support corporate policies and strategies. The rapid evolution of research leads to the development of multiple research communities, making it difficult for the uninitiated to navigate the literature. Indeed, system reliability encompasses several research subfields that focus on maximizing the life cycle of assets, including Reliability, Availability, Maintainability, and Safety (RAMS), Prognostics and Health Management (PHM), and Engineering of Asset Management. This article proposes a review of these concepts with the aim of identifying the different scientific communities, what differentiates them, and what connects them. It also addresses RAMS and PHM modeling techniques and highlights the significance of these disciplines in ensuring the functioning of complex systems. In summary, this article aims to clarify the interrelationship between the topics of reliability engineering, to simplify the search and selection for modeling methods.

Type de document:	Article
Mots-clés libres:	Engineering of asset management Modeling Prognostic and health management Reliability Data processing Democratization Life cycle analysis Numerical model Reliability analysis Strategic approach
Date de dépôt:	16 juill. 2025 14:49
Dernière modification:	16 juill. 2025 14:49
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/12113

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