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The Role of Artificial Intelligence in Predictive Maintenance for Industrial Engineering Systems
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Industrial Engineering
Abstract
Predictive maintenance has emerged as a transformative strategy in industrial engineering, enabling early detection of equipment failures and minimizing unplanned downtime. The integration of Artificial Intelligence (AI) into predictive maintenance systems enhances their efficiency by leveraging machine learning, deep learning, and real-time data analytics. This paper explores the role of AI in predictive maintenance within industrial systems, examining intelligent models that process sensor data, identify failure patterns, and estimate Remaining Useful Life (RUL). The study reviews current AI methodologies, industrial case applications, benefits, challenges, and future research directions. Findings suggest that AI-driven predictive maintenance improves reliability, optimizes maintenance scheduling, and reduces costs, contributing to smarter and more sustainable industrial operations.
Keywords
Artificial Intelligence, Predictive Maintenance, Industrial Engineering, Machine Learning, Industrial IoT
References
[1] Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2019). A proactive decision making framework for condition-based maintenance. Industrial Management & Data Systems, 119(3), 473–495. https://doi.org/10.1108/IMDS-09-2018-0404
[2] Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing conditionbased maintenance. Mechanical Systems and Signal Processing, 20(7), 14831510.https://doi.org/10.1016/j.ymssp.2005.09.012
[3] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing,104,799834.https://doi.org/10.1016/j.ymssp.2017.10.016
[4] Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning (2nd ed.). MIT Press. Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: A review. The International Journal of Advanced Manufacturing Technology, 50, 297–313.https://doi.org/10.1007/s00170-009-2482-0
[5] Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on IndustrialInformatics,11(3),812820https://doi.org/10.1109/TII.2014.2349359
[6] 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.2018.2813800
[7] Zhou, F., Qin, Y., Luo, X., & Yin, Y. (2021). Edge intelligence for industrial predictive maintenance: Opportunities and challenges. Journal of Manufacturing Systems, 60, 158–170.https://doi.org/10.1016/j.jmsy.2021.05.007
How to cite this paper
@article{1709941,
author = {Mohammed Abdus Salam},
title = {The Role of Artificial Intelligence in Predictive Maintenance for Industrial Engineering Systems},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {6},
pages = {1121-1124},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709941.pdf},
abstract = {Predictive maintenance has emerged as a transformative strategy in industrial engineering, enabling early detection of equipment failures and minimizing unplanned downtime. The integration of Artificial Intelligence (AI) into predictive maintenance systems enhances their efficiency by leveraging machine learning, deep learning, and real-time data analytics. This paper explores the role of AI in predictive maintenance within industrial systems, examining intelligent models that process sensor data, identify failure patterns, and estimate Remaining Useful Life (RUL). The study reviews current AI methodologies, industrial case applications, benefits, challenges, and future research directions. Findings suggest that AI-driven predictive maintenance improves reliability, optimizes maintenance scheduling, and reduces costs, contributing to smarter and more sustainable industrial operations.},
keywords = {Artificial Intelligence, Predictive Maintenance, Industrial Engineering, Machine Learning, Industrial IoT},
month = {December},
}