A COMPREHENSIVE FRAMEWORK FOR IOT-DRIVEN PREDICTIVE MAINTENANCE: LEVERAGING AI AND EDGE COMPUTING FOR ENHANCED EQUIPMENT RELIABILITY

Mohammad Hamasha; Qais  Albedoor; Sa'd Hamasha; Haneen  Ali; Ahmad  Qamar; Fateh  Barrah

doi:10.5937/jaes0-57002

Mohammad Hamasha Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan
Qais Albedoor Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan
Sa'd Hamasha Department of Industrial Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, Jordan
Haneen Ali Department of Mechanical and Industrial Engineering, Applied Science Private University, Amman, Jordan
Ahmad Qamar Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan
Fateh Barrah Department of Process Engineering, Faculty of Technology, University of 20Août 1955, Skikda, Algeria

DOI: https://doi.org/10.5937/jaes0-57002

Keywords: Internet of Thing, predictive maintenance, PdM, machine learning

Abstract

The convergence of the Internet of Things (IoT), Artificial Intelligence (AI), and Edge Computing has advanced predictive maintenance (PdM). The main two benefits of this integration are to enable real-time monitoring and proactive equipment management across industries. This paper presents a comprehensive framework for IoT-driven PdM, using AI-powered analytics and Edge Computing to enhance equipment reliability, reduce operational downtime, and optimize maintenance costs. Based on a comprehensive study of the previous work, we proposed a framework that integrates six key steps to use IoT, AI, and edge computing in preventive maintenance. The steps are IoT sensors and devices for data acquisition, Edge and cloud computing for efficient processing, AI-driven predictive analytics for fault detection, automated decision-making and alert systems, remote monitoring and automated control, and continuous learning for system optimization. The paper discussed the advantages of the proposed approach, such as reduced costs, and improved instrument utilization. However, challenges such as cybersecurity concerns, integration complexities, and computational resource requirements are also presented. A case study involving the implementation of an IoT-based PdM system for water tank trucks in a Civil Defense Directorate demonstrates the effectiveness of the proposed framework in real-world applications. Results show that real-time data analytics and predictive modeling improve problem detection accuracy, enabling prompt intervention and minimizing expensive mechanical breakdowns. This study proposes a systematic approach to AI-enabled PdM adoption, enabling scalable and cost-effective industrial maintenance strategy optimization.

References

Thakkar, D., Kumar, R. (2024). AI-Driven Predictive Maintenance for Industrial Assets using Edge Computing and Machine Learning. Journal of Research and Applied Sciences and Biotechnology, 3, 363–367.

Stadnicka, D., Sęp, J., Amadio, R., Mazzei, D., Tyrovolas, M., Stylios, C., Carreras-Coch, A., Merino, J. A., Żabiński, T., Navarro, J. (2022). Industrial needs in the fields of artificial intelligence, Internet of Things and edge computing. Sensors, 22(12), 4501.

Jardine, A. K. S., Lin, D., Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012

D’Agostino, P., Violante, M., Macario, G. (2024). A Scalable Fog Computing Solution for Industrial Predictive Maintenance and Customization. Electronics, 14(1), 24.

Sathupadi, K., Achar, S., Bhaskaran, S. V., Faruqui, N., Abdullah-Al-Wadud, M., Uddin, J. (2024). Edge-cloud synergy for AI-enhanced sensor network data: A real-time predictive maintenance framework. Sensors, 24(24), 7918.

Cheikh, K., Boudi, E. M., Rabi, R., Mokhliss, H. (2024). Balancing the maintenance strategies to making decisions using Monte Carlo method. MethodsX, 13, 102819.

Mourtzis, D., Angelopoulos, J., Panopoulos, N. (2022). Design and development of an edge-computing platform towards 5G technology adoption for improving equipment predictive maintenance. Procedia Computer Science, 200, 611–619.

Bala, A., Jusoh, A. R. Z., Ismail, I., Oliva, D., Muhammad, N., Sait, S. M., Al-Utaibi, K. A., Amosa, T. I., Memon, K. A. (2024). Artificial intelligence and edge computing for machine maintenance-review. Artificial Intelligence Review, 57(5), 119.

Zheng, H., Paiva, A. R., Gurciullo, C. S. (2020). Advancing from predictive maintenance to intelligent maintenance with AI and IIoT. arXiv preprint arXiv:2009.00351.

Hafeez, T., Xu, L., Mcardle, G. (2021). Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access, 9, 49355–49371.

Tau, A. L., Edoun, E. I., Mbohwa, C., Pradhan, A. (2023). Improvement of Overall Equipment Effectiveness Through Planned Equipment Maintenance: A Case Study. The Business and Management Review, 14(3).

Kayalvizhi, S., Manoharan, S., Muthukamatchi, P. K., Nagendran, R., Srithar, S., Nagalalli, G. (2025). Integrating Machine Learning and IoT for Real-Time PdM in Industrial Ecosystems: A Case Study Analysis. International Journal of Research in Industrial Engineering, 14(2), 385–409.

Ajayi, R. (2025). Integrating IoT and Cloud Computing for Continuous Process Optimization in Real Time Systems. International Journal of Research Publication and Reviews, 6(1), 2540–2558.

Astawa, I. P. P., Arsha, I. M. R. M. (2024). Artificial Intelligence and the Future of Work: Implications for Businesses and Employees. International Journal of Multidisciplinary Research and Publications (IJMRAP), 7(2), 31–35.

Dhinakaran, D., Raja, S. E., Velselvi, R., Purushotham, N. (2025). Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications. SN Computer Science, 6(2), 151.

Căuniac, D. A., Cîrnaru, A. A., Oprea, S. V., Bâra, A. (2025). Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach. Sensors (Basel, Switzerland), 25(3), 906.

Li, Z., Zheng, P., Tian, Y. (2025). Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing. Alexandria Engineering Journal, 119, 465–477.

Gupta, D. (2025). AI-Powered Predictive Maintenance in Smart City IoT Systems. Research Annals of Industrial and Systems Engineering, 2(1), 19–26.

Keshar, A. (2025). Advancing Industrial IoT and Industry 4.0 through Digital Twin Technologies: A comprehensive framework for intelligent manufacturing, real-time analytics and predictive maintenance. World Journal of Advanced Engineering Technology and Sciences, 14(1), 228–240.

Candón Fernández, E., Crespo Márquez, A., Guillén López, A. J., Hidalgo Fort, E. (2025). Framework for Asset Digitalization: IoT Platforms and Asset Health Index in Maintenance Applications. Applied Sciences, 15(3), 1524.

Lysenko, S., Лисенко, С. М. (2025). Predictive telemetry in the management of business processes in the auto transport industry. Business Navigator, 1(78).

Salam, M. H. u., Rathore, S. A., Maken, F. H., Janjua, J. I., Irfan, M. (2025). Harnessing Big Data and IoT for Enhanced Resource Optimization in Sustainable Construction within Smart Cities. The Journal of Research Review, 2(1), 90–104.

Surapaneni, P., Chigurupati, S., Bojjagani, S. (2025). Pioneering Healthcare with AIoT: Case Studies and Breakthroughs. In Future Innovations in the Convergence of AI and Internet of Things in Medicine, 1–22. IGI Global Scientific Publishing.

Ajayi, R. (2025). Integrating IoT and Cloud Computing for Continuous Process Optimization in Real Time Systems. International Journal of Research Publication and Reviews, 6, 2540–2558.

Cui, Y., Kara, S., Chan, K. C. (2020). Manufacturing big data ecosystem: A systematic literature review. Robotics and Computer-Integrated Manufacturing, 62, 101861.

Rajareddy, G. N. V., Mishra, K., Satti, S. K., Chhabra, G. S., Sahoo, K. S., Gandomi, A. H. (2025). A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification. Ecological Informatics, 103063.

Alemede, V. O. (2025). Process Technologies: Advancing Efficiency and Sustainability through Optimization and Control. International Journal of Research Publication and Reviews, 6(2), 1941–1955.

Mennilli, R., Mazza, L., Mura, A. (2025). Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study. Sensors (Basel, Switzerland), 25(2), 537.

Parma, T. (2022). Structured Problem-Solving Techniques for Manufacturing Datasets to Enhance Yield. Journal of Artificial Intelligence Machine Learning and Data Science, 1(1), 2161–2166. https://doi.org/10.51219/JAIMLD/tarun-parmar/473

Ledmaoui, Y., El Maghraoui, A., El Aroussi, M., Saadane, R. (2025). Review of Recent Advances in Predictive Maintenance and Cybersecurity for Solar Plants. Sensors, 25(1), 206.

Mugala, V. (2025). Leveraging Generative AI for Enhanced Predictive Maintenance and Anomaly Detection in Manufacturing. In Generative Artificial Intelligence (AI) Approaches for Industrial Applications, 261–279. Cham: Springer Nature Switzerland.

Klein, P., Malburg, L., Bergmann, R. (2025). Combining informed data-driven anomaly detection with knowledge graphs for root cause analysis in predictive maintenance. Engineering Applications of Artificial Intelligence, 145, 110152.

De Silva, D., Sierla, S., Alahakoon, D., Osipov, E., Yu, X., Vyatkin, V. (2020). Toward intelligent industrial informatics: A review of current developments and future directions of artificial intelligence in industrial applications. IEEE Industrial Electronics Magazine, 14(2), 57–72.

Sui, X., Lu, X., Ji, Y., Yang, Y., Peng, J., Li, M., Han, G. (2025). Intelligent Oil Production Management System Based on Artificial Intelligence Technology. Processes, 13(1), 133.

de Filippis, R., Al Foysal, A. (2025). AI-Driven Early Warning and Risk Management System for Delirium in ICU Patients. Open Access Library Journal, 12(1), 1–17.

Coursey, A., Quinones-Grueiro, M., Alvarez, L., Biswas, G. (2025). Combining Reinforcement Learning and Cascade PID Control for UAV Disturbance Rejection. Available at SSRN 5120263.

Vitelli, M. (2024). Artificial Intelligence “at the Edge” for Resource-Constrained Devices Based on SENSIPLUS Technology.

Henneberry, D., Ziakkas, D. (2025). The role of Artificial Cognitive Systems in the Implementation of the Aviation Fatigue Risk Management Systems. In Intelligent Human Systems Integration (IHSI 2025): Integrating People and Intelligent Systems, 160(160).

Narsaiah, M. V. R., Saindane, S., Bhosale, N., Bhujbal, V., Yadav, R. K. B. (2024). Quality assurance of radiation early warning system (REWS) at nuclear facility. In Proceedings of the DAE-BRNS National Conference on Industrial Safety and Hygiene-Zero Incident Vision.

Soyka, F., Nickel, P., Rebelo, F., Lux, A., Grabowski, A. (2025). Use of AR/MR/VR in the Context of Occupational Safety and Health. Frontiers in Virtual Reality, 6, 1528804.

Jain, R. K., Samanta, S. K. (2025). Application of IoT and Artificial Intelligence in Smart Manufacturing: Towards Industry 4.0. In Handbook of Intelligent and Sustainable Manufacturing, 235–259. CRC Press.

Papalexiou, S. M., Mascaro, G., Pendergrass, A. G., Mamalakis, A., Madruga de Brito, M., Andreadis, K. M., Schiro, K., et al. (2025). Sustainability nexus AID: Storms. Sustainability Nexus Forum, 33(1), 1. Berlin/Heidelberg: Springer Berlin Heidelberg.

Lv, B., Gong, H., Dong, B., Wang, Z., Guo, H., Wang, J., Wu, J. (2025). An Explainable XGBoost Model for International Roughness Index Prediction and Key Factor Identification. Applied Sciences, 15(4), 1893.

Dey, M. T. (2025). Explainable Artificial Intelligence (XAI): Integration, Policy Frameworks, and Applications in Critical Domains and Renewable Energy. In Explainable Artificial Intelligence and Solar Energy Integration, 333–362. IGI Global.

Nazir, M. A., Sajjad, A., Ahmed, S., Sattar, A., Iqbal, T., Ahmad, S., Riaz, A., Akram, A. (2025). Hybrid Energy Systems for Heavy Vehicles: Combining Heat Recovery and Renewable Energy Sources. Spectrum of Engineering Sciences, 3(1), 374–400.

Bhagali, A. C., Mestri, M. N., Bavachkar, S. G., Mahadik, S. A., Rajmane, U. C. (2021). IoT-enabled Building Automation Systems: Challenges, Opportunities, and Case Studies in Energy Efficiency and User Comfort. International Journal on Recent and Innovation Trends in Computing and Communication, 9(12), 37–43.

Singh, D. S. (2025). Development of a Smart IoT-based System for Real-Time Monitoring and Control of Industrial Automation. Shodh Prakashan: Journal of Engineering & Scientific Research, 1(1), 1–11.

Syahputra, R. A. G. (2025). Pemodelan Engine Health Monitoring System Yang Berpengaruh Terhadap Predictive Maintenance Mesin Dengan Parameter RPM, Suhu, dan Tekanan Berbasis PLC. PhD diss., Institut Teknologi Sepuluh Nopember.

Gustafsson, B. (2024). Remote Maintainability Analysis of Elevator Automatic Doors. MS thesis, Insinööritieteiden Korkeakoulu.

Haroon, M., Tripathi, M. M. (2024). Exploiting of Electrical Energy and Artificial Intelligence for Automation. In International Conference on Signal, Machines, Automation, and Algorithm, 777–791. Springer, Singapore.

Shah, W., Best, T. (2025). Threat Detection and Risk Management: Using AI to Counter Cyber Threats. https://doi.org/10.13140/RG.2.2.18028.68481

Lee, H. (2025). Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers. Applied Sciences, 15(4), 1771.

Fowowe, O. O., Okolue, C. A. (2025). Advancing Healthcare Frameworks in the US: Artificial Intelligence Applications Across Operations and Administration. International Journal of Computer Applications Technology and Research, 14(2), 82–98.