Issue |
MATEC Web Conf.
Volume 401, 2024
21st International Conference on Manufacturing Research (ICMR2024)
|
|
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Article Number | 13001 | |
Number of page(s) | 6 | |
Section | Digital / Smart Manufacturing, and Industry 4.0 | |
DOI | https://doi.org/10.1051/matecconf/202440113001 | |
Published online | 27 August 2024 |
Leveraging industry 4.0 techniques for predictive equipment maintenance: From concept to commissioning
Department of Manufacturing and Industrial Engineering, University of Malta, Msida
* Corresponding author: andrea.bondin@um.edu.mt
In recent decades, lean manufacturing has significantly impacted the manufacturing industry, gaining widespread adoption. Companies have increasingly recognised the competitive advantages of implementing flow-oriented production layouts, demand-flow technologies, and just-in-time production. This shift has also transformed the approach to maintenance, moving away from reactive strategies. With production cells becoming more susceptible to system disturbances, maintenance managers are now focused on strategic maintenance development to ensure reliable production equipment. The emergence of Industry 4.0 technologies has further reshaped plant maintenance, providing companies with accurate and dependable tools for proactive maintenance. This paper proposes a hypothesis of research conducted at the University of Malta, focusing on the potential application of predictive maintenance (PdM) in the upkeep of automation machinery. It suggests the use of a novel data management and acquisition system, grounded in simulation and deep learning modelling, to predict the remaining useful life (RUL) of machinery early during the design machine realisation. This exploration lays the groundwork for potential development of a comprehensive maintenance tool. Such a tool would optimise automation designs, estimate maintenance costs throughout the machine’s lifecycle, and prevent machine breakdown through proactive interventions.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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