Published on: December 2025
COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR PREDICTIVE MAINTENANCE IN SMART MANUFACTURING
Pankaj A. Joshi Vikram S. Patil Rohan M. Deshmukh
Dr. Suresh K. Malhotra
Apex Institute of Engineering & Technology
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Abstract
In modern manufacturing environments, the integration of digital technologies has shifted the paradigm from reactive to proactive maintenance strategies. Predictive maintenance (PdM), powered by machine learning (ML) models, uses historical and real-time data to forecast equipment failures before they occur, thus optimizing maintenance schedules, reducing downtime, and minimizing operational costs. This research article provides a detailed examination and comparative evaluation of various machine learning models applied in predictive maintenance within smart manufacturing. It explores their performance, advantages, limitations, data requirements, and suitability for different types of industrial assets. We discuss systematic implementation roadmaps, data preprocessing techniques, model evaluation metrics, integration with industrial Internet of Things (IIoT), and challenges faced during deployment. The study also suggests how hybrid and ensemble approaches can further enhance prediction accuracy. Based on simulated and real-world case studies, we compare traditional ML methods such as Logistic Regression (LR), Support Vector Machines (SVM), and Random Forests (RF) with advanced deep learning architectures like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Autoencoders. Our experimental results indicate that deep learning models outperform classical algorithms in capturing temporal dependencies and nonlinear patterns in time-series sensor data, albeit at the expense of higher computational costs. We provide recommendations for model selection based on application requirements and data availability, along with future research directions in adaptive predictive maintenance frameworks.
To ensure effective deployment, it is crucial to address data quality issues such as noise, missing values, and class imbalance through robust preprocessing techniques. Additionally, aligning model complexity with available computational resources and real-time processing requirements is essential for practical implementation. Future work should focus on developing adaptive models that can learn continuously from streaming data to maintain prediction accuracy over time
How to Cite this Paper
Joshi, P. A., Patil, V. S. & Deshmukh, R. M. (2025). Comparative Analysis of Machine Learning Models for Predictive Maintenance in Smart Manufacturing. International Journal of Creative and Open Research in Engineering and Management, <i>01</i>(03), 1-9. https://doi.org/10.55041/ijcope.v1i3.001
Joshi, Pankaj, et al.. "Comparative Analysis of Machine Learning Models for Predictive Maintenance in Smart Manufacturing." International Journal of Creative and Open Research in Engineering and Management, vol. 01, no. 03, 2025, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v1i3.001.
Joshi, Pankaj,Vikram Patil, and Rohan Deshmukh. "Comparative Analysis of Machine Learning Models for Predictive Maintenance in Smart Manufacturing." International Journal of Creative and Open Research in Engineering and Management 01, no. 03 (2025): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v1i3.001.
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References
1. Zhang, W., Yang, D., & Wang, H. (2015). Data-driven methods for predictive maintenance of industrial equipment: A review. IEEE Systems Journal, 11(3), 812-825.
2. Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2017). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799-834.
3. Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2018). Long Short-Term Memory Networks for Anomaly Detection in Time Series. Proceedings of the 23rd ACM SIGKDD International Conference.
4. Wang, T., Yu, Y., & Yang, J. (2020). Machine learning approaches for predictive maintenance: A review. Journal of Manufacturing Systems, 54, 1-12.
5. Nguyen, T., Ding, M., & Seneviratne, A. (2022). Hybrid Models for Predictive Maintenance in Smart Manufacturing. International Journal of Prognostics and Health Management, 13(1), 45-61.
6. Bekar, E. T., Nyqvist, P., & Skoogh, A. (2014). An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Advances in Mechanical Engineering, 12(5), 168781402091920. https://doi.org/10.1177/1687814020919207
7. Besha, A. R. M. A., Ojekemi, O. S., Oz, T., & Adegboye, O. (2025). PLSCO: An Optimization-Driven Approach for Enhancing Predictive Maintenance Accuracy in Intelligent Manufacturing. Processes, 13(9), 2707. https://doi.org/10.3390/pr13092707
8. Taşcı, B., Omar, A., & Ayvaz, S. (2023). Remaining useful lifetime prediction for predictive maintenance in manufacturing. Computers & Industrial Engineering, 184, 109566. https://doi.org/10.1016/j.cie.2023.109566
9. Fathy, Y., Jaber, M., & Brintrup, A. (2020). Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis. IEEE Access, 9, 2734–2757. https://doi.org/10.1109/access.2020.3047838
10. Him, L. C., Poh, Y. Y., & Pheng, L. W. (2019). IoT-based Predictive Maintenance for Smart Manufacturing Systems. 1942–1944. https://doi.org/10.1109/apsipaasc47483.2019.9023106
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