Intelligent Predictive Maintenance Using Hybrid Artificial Intelligence Models for Sustainable Industrial Automation in Industry 4.0

Abstract

The rapid advancement of Industry 4.0 has transformed conventional industrial automation by integrating intelligent sensing, cyber-physical systems, industrial Internet of Things (IIoT), cloud computing, and advanced artificial intelligence technologies into manufacturing environments. Among these innovations, predictive maintenance has emerged as a strategic approach for improving equipment reliability, minimizing unexpected failures, reducing maintenance costs, and enhancing production efficiency. However, the increasing complexity of industrial systems requires more robust and adaptive prediction mechanisms than those offered by conventional machine learning models. Hybrid artificial intelligence models, combining deep learning, ensemble learning, optimization algorithms, and explainable decision-making techniques, provide improved fault diagnosis, remaining useful life estimation, and maintenance scheduling under dynamic operating conditions. Furthermore, intelligent predictive maintenance contributes significantly to sustainable industrial automation by reducing energy consumption, minimizing material waste, extending equipment lifespan, and supporting environmentally responsible manufacturing practices. This study proposes a comprehensive research framework that investigates the integration of hybrid artificial intelligence models into predictive maintenance systems for Industry 4.0. The proposed framework aims to improve prediction accuracy, operational reliability, resource utilization, and sustainability while enabling autonomous maintenance decisions in smart factories. The study further establishes performance evaluation metrics and implementation strategies for next-generation intelligent industrial maintenance systems.