Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
A Comparative Study of Machine Learning Algorithms for Predictive Maintenance in Manufacturing Systems
Dr. Praveen Kumar Mannepalli
15 July 2025 · Vol. 1, Issue 1, pp. 8-14
DOI: 10.65150/EP-gjetr/V1E1/2025-A002 (registration with Crossref in progress)
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What is this article about?
The global manufacturing sector is rapidly transitioning towards smarter, data-driven operations to enhance productivity, reduce operational costs, and improve overall equipment effectiveness. Predictive Maintenance (PdM) stands out as one of the most transformative approaches within this paradigm.
Article at a glance
- Published
- 15 July 2025
- Volume / issue / pages
- Vol. 1, Issue 1, pp. 8-14
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
The global manufacturing sector is rapidly transitioning towards smarter, data-driven operations to enhance productivity, reduce operational costs, and improve overall equipment effectiveness. Predictive Maintenance (PdM) stands out as one of the most transformative approaches within this paradigm. By anticipating equipment failures before they occur, PdM ensures that maintenance is carried out only when required, leading to optimal utilization of resources. Machine learning (ML) techniques form the core of PdM, leveraging data to detect patterns and anomalies that precede system failures. This paper presents a comprehensive comparative study of leading ML algorithms—both traditional and deep learning-based—deployed for PdM in manufacturing systems. Algorithms such as Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Multilayer Perceptrons, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs) are rigorously evaluated on standardized datasets. Each model is analyzed in terms of prediction accuracy, interpretability, computational efficiency, and suitability for deployment in industrial settings. The results reveal key trade-offs and offer recommendations tailored to different types of manufacturing environments. The paper also discusses the current challenges in PdM adoption and offers insights into future research directions, including the integration of explainable AI and real-time edge computing solutions.
Keywords: Predictive Maintenance, Manufacturing Systems, Machine Learning, Condition Monitoring, Deep Learning, Random Forest, LSTM, Support Vector Machine, Fault Detection, Industrial IoT
Cite this article
Dr. Praveen Kumar Mannepalli (2025). A Comparative Study of Machine Learning Algorithms for Predictive Maintenance in Manufacturing Systems. Global Journal of Engineering and Technology Research, 1(1), 8-14. https://www.ep-journals.org/articles/a-comparative-study-of-machine-learning-algorithms-for-predictive-maintenance-d80c4dd3
@article{Dr2025,
title = {A Comparative Study of Machine Learning Algorithms for Predictive Maintenance in Manufacturing Systems},
author = {Dr. Praveen Kumar Mannepalli},
journal = {Global Journal of Engineering and Technology Research},
year = {2025},
volume = {1},
number = {1},
pages = {8-14},
url = {https://www.ep-journals.org/articles/a-comparative-study-of-machine-learning-algorithms-for-predictive-maintenance-d80c4dd3}
}Questions about this article
Who wrote this article?
Dr. Praveen Kumar Mannepalli.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 1, Issue 1, pages 8-14, published by EP Journals Group on 15 July 2025.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V1E1/2025-A002. Registration with Crossref is in progress.
How do I cite this article?
APA: Dr. Praveen Kumar Mannepalli (2025). A Comparative Study of Machine Learning Algorithms for Predictive Maintenance in Manufacturing Systems. Global Journal of Engineering and Technology Research, 1(1), 8-14. https://www.ep-journals.org/articles/a-comparative-study-of-machine-learning-algorithms-for-predictive-maintenance-d80c4dd3
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