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Open Access Journal Article

Machine Learning Approaches for Predictive Maintenance in Manufacturing

by David White 1,*
1
David White
*
Author to whom correspondence should be addressed.
TET  2022, 35; 4(2), 35; https://doi.org/10.69610/j.tet.20221222
Received: 14 October 2022 / Accepted: 17 November 2022 / Published Online: 22 December 2022
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Abstract

The integration of machine learning techniques into predictive maintenance processes has revolutionized the manufacturing industry, enhancing the efficiency and reliability of equipment by allowing for early detection of potential issues. This paper explores various machine learning approaches that have been successfully implemented in predictive maintenance systems. It begins by outlining the fundamental principles of machine learning and how they can be applied to predictive maintenance. Subsequently, the paper delves into the different algorithms used, including supervised learning methods such as decision trees, support vector machines, and neural networks, as well as unsupervised learning techniques like clustering and anomaly detection. Additionally, the paper discusses the challenges associated with data acquisition, preprocessing, and feature selection, which are critical for the success of predictive maintenance models. The effectiveness of these machine learning approaches is exemplified through case studies from the automotive, aerospace, and energy sectors. The paper concludes by emphasizing the need for continuous model refinement and adaptation to ensure the ongoing relevance and reliability of predictive maintenance systems in modern manufacturing environments.

 

Keywords: Machine Learning, Predictive Maintenance, Manufacturing Industry, Algorithmic Approaches, Data Preprocessing.;
Copyright: © 2022 by White. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
White, D. Machine Learning Approaches for Predictive Maintenance in Manufacturing. Transactions on Engineering and Technology, 2022, 4, 35. https://doi.org/10.69610/j.tet.20221222
AMA Style
White D. Machine Learning Approaches for Predictive Maintenance in Manufacturing. Transactions on Engineering and Technology; 2022, 4(2):35. https://doi.org/10.69610/j.tet.20221222
Chicago/Turabian Style
White, David 2022. "Machine Learning Approaches for Predictive Maintenance in Manufacturing" Transactions on Engineering and Technology 4, no.2:35. https://doi.org/10.69610/j.tet.20221222
APA style
White, D. (2022). Machine Learning Approaches for Predictive Maintenance in Manufacturing. Transactions on Engineering and Technology, 4(2), 35. https://doi.org/10.69610/j.tet.20221222

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