AI-Driven Predictive Maintenance for Industrial Assets using Edge Computing and Machine Learning

Abstract

The increasing complexity and scale of industrial assets, such as machinery, equipment, and infrastructure, have led to a growing need for effective predictive maintenance strategies. Traditional time-based or reactive maintenance approaches often fall short in addressing the dynamic nature of asset degradation and failure patterns. This study explores the integration of artificial intelligence (AI) and machine learning (ML) algorithms with edge computing to develop an intelligent predictive maintenance framework for industrial assets. By processing sensor data and executing ML models closer to the source, at the edge, this approach enables real-time anomaly detection, remaining useful life (RUL) estimation, and proactive maintenance scheduling. The paper outlines the key methods involved, including sensor data preprocessing, feature engineering, ML model development, and deployment on edge devices. It also discusses the benefits of this integration, such as reduced downtime, improved asset reliability, and enhanced operational efficiency. Furthermore, the study highlights emerging trends, such as transfer learning, ensemble modeling, and adaptive learning, which enhance the flexibility, accuracy, and adaptability of the AI-driven predictive maintenance system. The findings demonstrate the transformative potential of this synergy, empowering industrial operations to transition from reactive to predictive maintenance, ultimately optimizing asset performance and reducing maintenance costs.