Integrating IoT Data Streams with Machine Learning for Predictive Maintenance in Industrial Systems

Sandip J. Gami; Dr. Seema Nath Jain

Integrating IoT Data Streams with Machine Learning for Predictive Maintenance in Industrial Systems

Authors

Sandip J. Gami
Dr. Seema Nath Jain

Abstract

This research paper investigates the integration of Internet of Things (IoT) data streams with machine learning algorithms to enable predictive maintenance in industrial systems. As industrial operations become increasingly automated and interconnected, the volume of real-time data generated by IoT devices offers a valuable resource for optimizing maintenance strategies. The study focuses on developing a framework that collects and analyzes data from various sensors embedded in industrial equipment to predict potential failures before they occur. Machine learning models are employed to process and interpret the data, identifying patterns and anomalies that signal impending issues. The paper discusses the design and implementation of the predictive maintenance system, highlighting the benefits of reduced downtime, extended equipment life, and cost savings. Additionally, it addresses the challenges associated with data integration, model accuracy, and the deployment of predictive analytics in complex industrial environments. Through empirical analysis and case studies, the research demonstrates the effectiveness of this approach in improving maintenance efficiency and reliability in industrial operations.

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Integrating IoT Data Streams with Machine Learning for Predictive Maintenance in Industrial Systems