BEST PRACTICES FOR BUILDING AI-DRIVEN PREDICTIVE MAINTENANCE SYSTEMS

Abstract

This comprehensive article explores implementing AI-driven predictive maintenance systems in industrial settings, focusing on best practices and essential components. The article examines how modern manufacturing facilities have improved operational efficiency through predictive maintenance strategies. It covers critical aspects, including data collection infrastructure, feature engineering, model selection, real-time integration, optimization, and implementation practices. The article demonstrates how organizations leveraging AI-powered predictive maintenance achieve substantial reductions in maintenance costs, improved equipment longevity, and enhanced operational efficiency. The article also highlights the importance of proper sensor deployment, data quality management, and cross-functional integration in successful implementations.