Integrating Big Data Analytics and Machine Learning for Predictive Maintenance within Industrial IoT Frameworks

With the proliferation of Industrial Internet of Things (IIoT) devices, there is an unprecedented growth in the volume, velocity, and variety of data generated from diverse sources. This paper presents a comprehensive study on the application of big data analytics for predictive maintenance in IIoT systems. By leveraging advanced machine learning and data analytics techniques, we propose a novel framework, Hybrid-sense that anticipates potential failures and optimizes maintenance schedules, thereby reducing downtime and operational costs. Our approach integrates multi-modal data sources, including sensor readings, operational logs, and environmental factors, to create a holistic predictive model. We employ a hybrid architecture combining both traditional statistical methods and deep learning algorithms to enhance prediction accuracy. The results, validated on real-world industrial datasets, demonstrate a significant improvement in early fault detection and maintenance prediction over existing methodologies. This research not only paves the way for more resilient and efficient IIoT systems but also offers insights into the intricate interplay between big data and industrial operations, emphasizing the transformative potential of datadriven decision-making in the manufacturing sector.