Integrating IoT and Modelling for Predictive Maintenance in Industry 4.0
DOI:
https://doi.org/10.63876/ijtm.v1i3.114Keywords:
Internet of Things (IoT), Predictive Maintenance, Digital Twin, Machine Learning, Industry 4.0, Proactive Maintenance SchedulingAbstract
This research presents an innovative approach to predictive maintenance by integrating Internet of Things (IoT) technology with advanced analytical modeling within the Industry 4.0 framework. The proposed system harnesses real-time data acquired from IoT sensors and combines it with machine learning algorithms and digital twin simulations to facilitate early detection of potential equipment failures. This hybrid strategy enables proactive maintenance scheduling, significantly reducing unplanned downtime and operational costs. A case study in the manufacturing sector illustrates that the interdisciplinary integration of sensor-based data and intelligent modelling not only enhances operational efficiency but also supports digital transformation by providing a flexible and responsive framework for addressing complex industrial challenges. The primary contribution of this study is the seamless unification of real-time data acquisition and predictive analytics, which lays the groundwork for the next generation of comprehensive predictive maintenance systems in the Industry 4.0 era.
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