Application of Internet of Things (IoT) Technology in Predictive Maintenance of Industrial Machines to Improve Operational Reliability

Abstract

The advancement of Industry 4.0 has spurred the integration of Internet of Things (IoT) technologies into predictive maintenance systems, particularly in industrial machinery management. Traditional maintenance models—reactive and time-based—have proven insufficient in addressing operational uncertainties and unexpected equipment failures. This study aims to explore how IoT applications in predictive maintenance can enhance the operational reliability of industrial machines. Utilizing a qualitative literature review method, the research synthesizes findings from 10 scholarly articles published between 2023 and 2025, focusing on various IoT applications such as digital twins, intelligent networks, and energy-optimized communication protocols. The data collection involved systematic searches through major academic databases using targeted keywords, and data analysis was conducted using thematic analysis to extract key themes such as architecture, machine learning integration, and infrastructure readiness. The findings reveal that IoT significantly contributes to predictive maintenance through real-time data collection, anomaly detection using AI models, and automated decision-making processes. The use of digital twins, fog computing, and sensor-integrated systems has demonstrated measurable improvements in equipment uptime, fault detection accuracy, and cost efficiency. The integration of IoT with Enterprise Asset Management (EAM) platforms has further enabled organizations to transform their maintenance strategies into data-driven, proactive systems. In conclusion, the study confirms that IoT is not merely a supporting technology but a foundational enabler of operational resilience in modern industries. This research provides a framework for sustainable implementation and highlights critical success factors such as sensor quality, model precision, and infrastructure scalability.