The integration of Internet of Things (IoT) technology in vibration analysis has revolutionized preventive maintenance strategies for mechanical structures. Traditional maintenance techniques, often based on fixed schedules or reactive repairs, have limitations in detecting issues before they lead to failures. This paper explores the application of IoT-enabled vibration sensors for real-time monitoring and analysis of mechanical structures, allowing for early detection of faults and enabling predictive maintenance. The proposed system collects vibration data from strategically placed sensors, processes it using advanced signal processing techniques, and analyzes it through machine learning algorithms to identify potential failure modes. Results indicate that IoT-integrated vibration analysis can significantly improve the efficiency of maintenance operations, reduce downtime, and optimize maintenance schedules. Additionally, the proposed method provides a scalable solution that can be adapted to a variety of mechanical systems. This paper concludes that IoT-enabled vibration analysis offers a promising approach for enhancing preventive maintenance in industries that rely on mechanical structures, with the potential for future advancements in system integration and fault detection accuracy.

Internet of Things (IoT), vibration analysis, preventive maintenance, mechanical structures, structural health monitoring (SHM), vibration sensors, condition monitoring, predictive maintenance, signal processing.