Journal of Cloud Computing and Internet of Things

Authors: Sandeep K. Patil, Ishita T. Mukherjee

Abstract: The integration of Artificial Intelligence (AI), Industrial Internet of Things (IIoT), and cloud computing has transformed traditional maintenance systems in industries. Predictive maintenance (PdM) leverages machine learning algorithms to forecast equipment failures before they occur, using real-time data collected from IoT sensors. This paper discusses how AI-driven predictive maintenance frameworks are built on cloud platforms, enabling scalable storage, efficient processing, and real-time insights. The research highlights the end-to-end architecture involving sensor data acquisition, cloud-based processing, and predictive model deployment. Case studies from manufacturing and infrastructure monitoring domains are also analyzed. The results emphasize the reduction of unplanned downtime, improved asset lifecycle, and cost-effectiveness brought by AI-based PdM systems.

Keywords: Predictive Analytics, Machine Learning, Industrial IoT, Cloud Storage, Sensor Data, Equipment Monitoring, Smart Maintenance