Real-Time Predictive Maintenance in Manufacturing: Leveraging IoT and Big Data Analytics

Abstract

This study explores the effectiveness of the Manufacturing Industry of The Internet of Things (IoT) and Big Data Analytics in Predictive Maintenance. Traditional maintenance practices such as preventive or corrective maintenance are often unable to effectively alleviate unplanned downtime and operational inefficiency. Therefore, this paper proposes an approach to real-time IoT sensors combined with advanced data processing methods. By combining the strengths of machine learning algorithms like Random Forests and Support Vector Machines with an A/B testing framework aimed at improving prediction model performance, this comprehensive strategy will be better able to predict failure. In doing so, this approach promises both higher predictive accuracy and substantially lower operational expenses. Our research sets the stage for revolutionary changes in maintenance. We see the potential of large decreases in downtime and maintenance expenditures to come. Future research will be aimed at verifying these methods in various manufacturing environments and introducing the latest AI technologies to further hone predictive models. As such, this paper uses case studies from conventional industries to propose a basic structure for advanced IoT-driven predictive maintenance systems.