Doorsamy, W. orcid.org/0000-0001-9043-9882 (2025) Condition Monitoring of Electric Machines: Modern Frameworks and Data-Driven Methodologies. Machines, 13 (2). 144. ISSN 2075-1702
Abstract
Electrical machines are at the centre of most engineering processes, with rotating electrical machines, in particular, becoming increasingly important in recent history due to their growing applications in electric vehicles and renewable energy. Although the landscape of condition monitoring in electrical machines has evolved over the past 50 years, the intensification of engineering efforts towards sustainability, reliability, and efficiency, coupled with breakthroughs in computing, has prompted a data-driven paradigm shift. This paper explores the evolution of condition monitoring of rotating electrical machines in the context of maintenance strategy, focusing on the emergence of this data-driven paradigm. Due to the broad and varying nature of condition monitoring practices, a framework is also offered here, along with other essential terms of reference, to provide a concise overview of recent developments and to highlight the modern challenges and opportunities within this area. The paper is purposefully written as a tutorial-style overview for the benefit of practising engineers and researchers who are new to the field or not familiar with the wider intricacies of modern condition monitoring systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 by the author. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | condition monitoring; data-driven; rotating electrical machines; maintenance strategy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Feb 2025 12:36 |
Last Modified: | 19 Feb 2025 12:36 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/machines13020144 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223486 |