Integration of Machine Learning Algorithms for Predictive Maintenance in IoT-Enabled Smart Safety Helmets

This paper focuses on the combination of ML algorithms for predictive maintenance through Internet of Things (IoT)-based smart safety helmets in order to increase not only the level of safety operations and but as well equipment life in industrial settings. We study the present helmet technologies and reveal the weaknesses by tomorrow foreseeing technologies which exist in current predictive maintenance strategies. Proposing a new technique, ML algorithms suite is applied as an analyzing tool to collected data from sensors within the helmets. The system architecture is real-time data processed by an IoT framework to predict and act on maintenance needs up front. The results of the experimental validation confirm the implementation of our approach, that shows the significant increase of the predictive accuracy and timeliness as compared to the traditional methods. The research has not only enabaled to create smart safety equipment but also a scalable approach that could be maximized across other Internet of things applications.