AI-DRIVEN CLOUD STORAGE SYSTEMS FOR PREDICTIVE MAINTENANCE IN MANUFACTURING

Abstract

This article examines the implementation and impact of AI-driven cloud storage systems for predictive maintenance in the manufacturing sector, focusing on case studies from industry leaders Siemens and General Electric (GE). The research explores the architecture of these systems, including IoT sensor deployment, data collection mechanisms, and AI algorithms for analysis. Through a comprehensive literature review and mixed-methods data collection approach, the study reveals significant benefits of these technologies, including a 30% reduction in unplanned downtime and substantial equipment lifespan extension. The article discusses the transformation of the manufacturing industry towards fully automated, self-monitoring production facilities and the resulting operational cost reductions and efficiency improvements. Additionally, it addresses key challenges such as implementation barriers, data security concerns, and workforce adaptation needs. The findings demonstrate the pivotal role of AI-driven predictive maintenance in advancing Industry 4.0 initiatives and highlight its potential to revolutionize manufacturing processes. This article contributes to the growing body of knowledge on smart manufacturing technologies and provides valuable insights for industry practitioners and researchers alike.