MECHANICAL & APPLIED SCIENCE DOMAIN

There is a newer version of the record available.

Published November 14, 2025 | Version v1
Journal article Open

AI-Driven Predictive Maintenance for Smart Manufacturing Systems

Authors/Creators

Description

In the era of Industry 4.0, smart manufacturing systems demand high reliability, reduced downtime, and cost-effective operations. Traditional maintenance strategies, such as reactive and preventive maintenance, often lead to inefficiencies and unplanned breakdowns. This research explores the integration of Artificial Intelligence (AI) techniques—such as machine learning, deep learning, and data analytics—for predictive maintenance in smart manufacturing environments. The study highlights how real-time sensor data and historical equipment records can be leveraged to forecast failures before they occur. A framework is proposed using supervised learning models, including decision trees and neural networks, to predict health equipment and schedule timely interventions. The results indicate a significant improvement in operational uptime, maintenance cost reduction, and overall equipment effectiveness (OEE). This paper concludes that AI-driven predictive maintenance plays a crucial role in transforming traditional manufacturing systems into intelligent, self-monitoring infrastructures.

Files

AI-Driven Predictive Maintenance for Smart Manufacturing Systems.pdf

Files (302.8 kB)

Additional details

References

  • Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3), 812–820. https://doi.org/10.1109/TII.2014.2349359
  • Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3), 2213–2227. https://doi.org/10.1109/JSYST.2019.2905565
  • Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53(6), 1517–1525. https://doi.org/10.1109/TIM.2004.834072
  • Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47. https://doi.org/10.1016/j.ymssp.2018.02.016
  • Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008, October). Damage propagation modeling for aircraft engine run-to-failure simulation. Proceedings of the International Conference on Prognostics and Health Management (PHM 2008). IEEE. https://doi.org/10.1109/PHM.2008.4711414
  • Ahmed, M. S., Kim, D. Y., & Kim, H. K. (2020). AI-based real-time predictive maintenance system for smart factory. Sensors, 20(3), 764. https://doi.org/10.3390/s20030764
  • Wu, B., Lin, T., & Kuo, M. H. (2020). Edge computing for predictive maintenance: Challenges and opportunities. Journal of Manufacturing Systems, 56, 48–59. https://doi.org/10.1016/j.jmsy.2020.04.017
  • Goebel, K., Saha, B., Saxena, A., Celaya, J. R., & Christophersen, J. P. (2008, March). Prognostics in battery health management. Proceedings of the IEEE Aerospace Conference. https://doi.org/10.1109/AERO.2008.4526414
  • Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2020). Machine health monitoring using deep learning: A survey. IEEE Transactions on Industrial Electronics, 68(3), 2581–2592. https://doi.org/10.1109/TIE.2020.3015153
  • Glaessgen, E., & Stargel, D. (2012, April). The digital twin paradigm for future NASA and U.S. Air Force vehicles. Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. https://doi.org/10.2514/6.2012-1818