AI-Based Predictive Maintenance for U.S. Manufacturing: Reducing Downtime and Increasing Productivity

Abstract

Predictive maintenance (PdM) has emerged as a critical strategy in the U.S. manufacturing sector, driven by advancements in artificial intelligence (AI) and machine learning. This study explores the implementation of AI-based predictive maintenance systems to minimize equipment downtime and enhance productivity. Through a comprehensive analysis of various AI algorithms, including supervised learning, neural networks, and time series forecasting, the research identifies key factors influencing maintenance decisions. By leveraging real-time data from Internet of Things (IoT) sensors, manufacturers can predict equipment failures and schedule maintenance activities more efficiently, thus optimizing operational workflows. The findings demonstrate significant reductions in unplanned downtime, leading to improved overall equipment effectiveness (OEE) and substantial cost savings. Furthermore, this research underscores the importance of integrating predictive maintenance within the broader context of Industry 4.0, highlighting its role in driving innovation and competitiveness in U.S. manufacturing. Ultimately, the study aims to provide practical insights and a framework for manufacturing firms seeking to adopt AI-driven maintenance strategies, paving the way for a more resilient and productive industry.