Krishna Menon, Madhu, and Tuladhar, Rabin (2024) Asset Management decision-making through data-driven Predictive Maintenance – an overview, techniques, benefits and challenges. Maintenance, Reliability and Condition Monitoring, 4 (2). pp. 44-63.

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Abstract

Over the years, industrial asset management has significantly transformed from being an unavoidable resource consumer to a value creator involving multi-criteria decision-making and optimisation. This is particularly important in the scenario of Industry 4.0, which offers more opportunities for improved maintenance effectiveness. This review examines the literature covering the evolving area of data-driven Predictive Maintenance (PdM) within engineering asset management. The work explores current and emerging practices for managing asset degradation, with emphasis on the domain of Prognostics and Health Management (PHM). Next, it examines the opportunities for data-driven methods, associated techniques, and data sources to incorporate data-driven PdM into the maintenance decision-making portfolio. The text concludes by discussing the opportunities and constraints related to data-driven PdM for three identified asset data streams. The paper offers insights for researchers and practitioners interested in utilising data-driven approaches to improve asset reliability, improve maintenance strategies and manage asset complexities.

Item ID:	84536
Item Type:	Article (Research - C1)
ISSN:	2669-2961
Copyright Information:	Copyright © 2024 Madhu Krishna Menon, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Date Deposited:	21 Feb 2025 04:54
FoR Codes:	40 ENGINEERING > 4014 Manufacturing engineering > 401404 Industrial engineering @ 80% 40 ENGINEERING > 4010 Engineering practice and education > 401006 Systems engineering @ 20%
SEO Codes:	24 MANUFACTURING > 2412 Machinery and equipment > 241204 Industrial machinery and equipment @ 100%
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