AI-Based Predictive Maintenance in Industrial Robotics

Abstract

The increasing reliance on industrial robotics across manufacturing sectors has made equipment reliability and uptime critical. Traditional maintenance approaches are reactive or schedule-based, often resulting in unexpected downtimes and increased operational costs. This paper explores the integration of Artificial Intelligence (AI) into predictive maintenance frameworks to address these challenges. By leveraging machine learning and deep learning models on sensor data, AI systems can detect early signs of wear, forecast component failures, and optimize maintenance scheduling. A comprehensive methodology is presented, including data acquisition, model training, and deployment, validated through experimental results on robotic joint datasets. The findings demonstrate significant improvements in fault prediction accuracy and lead time. This research highlights the transformative potential of AI in enabling intelligent, self-monitoring robotic systems within Industry 4.0 environments.