An IoT-Driven Predictive Maintenance Model for Smart Manufacturing Systems

This paper implements an Internet of Things (IoT)-fueled predictive maintenance paradigm aimed at improving the effectiveness and dependability of smart manufacturing systems. Using machine learning technologies, the model predicts failures and schedules maintenance based on sensor data captured from industrial machines and equipment. The methodology encompasses activities in real-time data capture, feature extraction, model training, and a decision support system, which aids in critical decision-making processes. Results proved there is a significant diminution in both maintenance expenditures and equipment downtime, thus validating the model's capacity to enhance productivity through advanced manufacturing systems.