AI-Enabled Self-Healing Cyber-Physical Systems for Industrial Automation

Abstract

Cyber-Physical Systems (CPSs) are transforming Industry 4.0 by integrating computation, networking, and physical processes to enable intelligent industrial automation. However, increasing system complexity introduces challenges related to reliability, fault tolerance, cybersecurity, and maintenance. This study proposes an AI-enabled self-healing CPS framework that supports autonomous fault detection, diagnosis, prediction, and recovery. The framework combines machine learning, deep learning, digital twin technology, and reinforcement learning to continuously monitor data from industrial sensors, PLCs, robotic systems, and network devices. A closed-loop architecture comprising monitoring, analysis, decision, action, and learning enables real-time anomaly detection and automated corrective actions such as parameter optimization, workload redistribution, and system reconfiguration without interrupting operations. By continuously learning from operational data, the framework enhances predictive maintenance, improves system resilience against hardware, software, and communication failures, and increases operational efficiency. The proposed approach provides a scalable foundation for next-generation smart manufacturing systems with enhanced autonomy, reliability, adaptability, and industrial intelligence.