Authors : Abdulmajeed Abdullatif Alomair

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/3ef44ccv

Scribd : https://tinyurl.com/ypapa6ke

DOI : https://doi.org/10.38124/ijisrt/25oct1028

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : This paper examines how the Industrial Internet of Things (IIoT) transforms maintenance practices in modern industries through predictive analytics and intelligent connectivity. It explains how IIoT integrates sensors, edge computing, cloud systems, and artificial intelligence to collect and process real-time data for equipment monitoring. Predictive maintenance (PdM) emerges as a data-driven strategy that minimizes unplanned downtime, extends asset life, and reduces operational costs by up to 25%. The paper outlines the technological architecture underpinning PdM, including digital twins, 5G networks, and deep learning models, and critically evaluates industrial adoption trends and barriers such as data silos, cybersecurity risks, and skill shortages. Empirical and industry evidence shows that PdM delivers measurable returns and drives sustainability within smart manufacturing ecosystems. Concluding insights emphasize that IIoT-enabled predictive maintenance is pivotal to achieving the efficiency, reliability, and resilience goals of Industry 4.0.

References :

1. Afrin, S, Rafa, S. J., Kabir, M., Farah, T., Bin, S., Aiman Lameesa, Ahmed, S. F., & Gandomi, A. H. (2025). Industrial Internet of Things: Implementations, challenges, and potential solutions across various industries. Computers in Industry, 170, 104317–104317. https://doi.org/10.1016/j.compind.2025.104317
2. ‌Brügge, F. (2023, November 29). Predictive maintenance market: 5 highlights for 2024 and beyond. IoT Analytics. https://iot-analytics.com/predictive-maintenance-market/
3. IIoT-World. (2024, May 14). Predictive maintenance: Cutting costs & downtime smartly. Retrieved October 16, 2025, from https://www.iiot-world.com/predictive-analytics/predictive-maintenance/predictive-maintenance-cost-savings/

4. ‌Ismail, L., Abdelmoti, A., Basu, A., Eddine, D., & Naouss, M. (2025). A Systematic Review of Digital Twin-Driven Predictive Maintenance in Industrial Engineering: Taxonomy, Architectural Elements, and Future Research Directions. ArXiv.org. https://arxiv.org/abs/2509.24443
5. MarketsandMarkets. (2024). Predictive Maintenance Market Report 2023–2028. Retrieved from https://www.marketsandmarkets.com/Market-Reports/operational-predictive-maintenance-market-8656856.html
6. McKinsey & Company. (2023, April 12). Manufacturing: Analytics unleashes productivity and profitability. Retrieved October 16, 2025, from https://www.mckinsey.com/capabilities/operations/our-insights/manufacturing-analytics-unleashes-productivity-and-profitability
7. McKinsey & Company. (2024, March 6). A smarter way to digitize maintenance and reliability. Retrieved October 16, 2025, from https://www.mckinsey.com/capabilities/operations/our-insights/a-smarter-way-to-digitize-maintenance-and-reliability
8. ‌Morgan, M. (2025). To Reduce Equipment Downtime, Manufacturers Turn to AI Predictive Maintenance Tools. Technology Solutions That Drive Business. https://biztechmagazine.com/article/2025/03/reduce-equipment-downtime-manufacturers-turn-ai-predictive-maintenance-tools
9. Sinha, S. (2024, September 3). State of IoT 2024: Number of connected IoT devices growing 13% to 18.8 billion globally. IoT Analytics. https://iot-analytics.com/number-connected-iot-devices/
10. Varalakshmi, K., & Kumar, J. (2025). Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-10268-8