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

  • Authors

    • José María Troya Professor of Computer Architecture, University of Málaga, Spain. Author
    • Ramon López de Mántaras AI Research Pioneer, IIIA-CSIC, Spain. Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2025PII1V9C

    Published 08-03-2025

  • Artificial Intelligence (AI), Cyber-Physical Systems (Cps), Self-Healing Systems, Industrial Automation, Industry 4.0, Predictive Maintenance, Machine Learning, Deep Learning, Digital Twin, Fault Diagnosis, Autonomous Recovery, Smart Manufacturing

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    AI-Enabled Self-Healing Cyber-Physical Systems for Industrial Automation. (2025). International Journal of Modern Research in Science & Engineering, 8(2), 01-15. https://doi.org/10.67228/30716357/IJMRSE-2025PII1V9C

  • 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.

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