Machine Learning for Industrial Predictive Maintenance

IPEM Journal Computer Applications > Machine Learning for Industrial Predictive Maintenance

Machine Learning for Industrial Predictive Maintenance

Dr. Mukta Makhija

Vol 9 , Issue 1 , December 2024 | Pages: 89-96

Published Online: December, 2024

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Author Details

1. Dr. Mukta Makhija, Associate Professor, Integrated Academy of Management & Technology, Ghaziabad, U.P, India

In the modern industrial landscape, predictive maintenance has emerged as a crucial strategy for ensuring operational efficiency, reducing downtime, and minimizing maintenance costs. Machine learning (ML) plays a pivotal role in enhancing predictive maintenance by leveraging vast amounts of data generated from industrial equipment and processes. This paper explores the application of machine learning algorithms in predictive maintenance, focusing on their ability to analyse sensor data, detect anomalies, and predict potential failures before they occur. By employing techniques such as regression, classification, clustering, and deep learning, ML models can identify pa erns and trends that are often imperceptible through traditional methods. These insights enable maintenance teams to proactively address issues, optimize maintenance schedules, and extend the lifespan of machinery. The integration of machine learning into predictive maintenance not only improves reliability and safety but also drives significant cost savings by preventing unplanned outages and reducing the need for unnecessary maintenance activities. As industries continue to evolve towards more data-driven approaches, the adoption of machine learning in predictive maintenance is expected to become increasingly indispensable, offering a competitive advantage in asset management and operational excellence.

Keywords

Predictive Maintenance (PdM), Operational Efficiency, Reinforcement Learning, Deep Learning, Neural Networks,

  1. B. Sadek, M. A. Ismail, and M. M. E. A. Mahmoud, “Machine Learning for Predictive Maintenance of Industrial Machines,” 2018 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 2018, pp. 1-8. DOI: 10.1109/ICCES.2018.8342870.
  2. Amruthnath and T. Gupta, “Machine Learning Algorithms for Predictive Maintenance in Manufacturing,” in Procedia Manufacturing, vol. 26, pp. 1115-1122, 2018. DOI: 10.1016/j.promfg.2018.07.146.
  3. Hamaide, D. Joassin, L. Castin, and F. Glineur, “A Two-Level Machine Learning Framework for Predictive Maintenance: Comparison of Learning Formulations,” arXiv preprint arXiv: 2204.10083, 2022. Available: h ps://arxiv.org/abs/2204.10083.
  4. F. Chevtchenko et al., “Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real Time IoT Data,” arXiv preprint arXiv:2310.14949, 2023. Available: h ps://arxiv.org/abs/2310.14949.
  5. Maheshwari, S. Tiwari, S. Rai, and S. V. D. P. Singh, “Comprehensive Study of Predictive Maintenance in Industries Using Classification Models and LSTM Model,” arXiv preprint arXiv:2403.10259, 2024. Available: h ps://arxiv.org/abs/2403.10259.
  6. Zhao, J. Yang, W. Wang, H. Yang, and D. Niyato, “TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework,” arXiv preprint arXiv:2309. 16935, 2023. Available: h ps://arxiv.org/abs/2309.16935.

K M. Makhija, “Machine Learning for Industrial Predictive Maintenance,” IPEM Journal of Computer Application & Research, vol. 9, pp. 89–96, Dec. 2024. DOI: