- Title
- Maintenance and asset management practices of industrial assets : importance of tribological practices and digital tools
- Creator
- Pai, Raghuvir; Chattopadhyay, Gopinath; Karmakar, Gour
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/192665
- Identifier
- vital:18050
- Identifier
-
https://doi.org/10.1504/IJPMB.2021.10052127
- Identifier
- ISSN:1460-6739 (ISSN)
- Abstract
- There are a large number of rotating and sliding parts in industrial assets. Tribological behaviour plays a significant role in influencing friction and wear, and in turn, the life of these parts. There are issues and challenges in understanding the tribological aspects and behaviour of machine components by maintenance professionals so that informed decisions can taken to improve performance and productivity. An understanding of tribology helps in developing and applying the tools and techniques necessary for better maintenance. In recent years, remote performance monitoring (RPM), internet of things (IoT), machine learning, artificial intelligence and data analytics have made a significant contribution to maintenance and asset management. This paper reviews the tribological aspects related to maintenance, reliability and asset management. The findings of this study will be useful to engineers and managers to understand and appreciate the relationship between tribology, maintenance, reliability and availability for better asset management. Copyright © 2023 Inderscience Enterprises Ltd.
- Publisher
- Inderscience Publishers
- Relation
- International Journal of Process Management and Benchmarking Vol. 13, no. 2 (2023), p. 233-256
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 Inderscience Enterprises Ltd
- Subject
- 3503 Business systems in context; 3507 Strategy, management and organisational behaviour; Artificial intelligence; Asset management; Data analytics; Internet of things; IoT; Machine learning; Maintenance; Tribology
- Reviewed
- Funder
- Authors are grateful to the Australian Government for funding under the Endeavour Executive Fellowship 2018 for Dr. Raghuvir Pai (supported by Dr. Gopinath Chattopadhyay, Dr. Gour Karmakar and Professor Steven Wilcox) to carry out professional development activities in Australia.
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