Self-Healing Industrial Networks Using AI-Enabled Fault Detection

  • Authors

    • Dr. Suresh Babu Reddy Professor, Osmania University, India. Author
    • Dr. Anita Verma Associate Professor, Banaras Hindu University, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2021PII9P2D

    Published 12-04-2021

  • Self-Healing Networks, Industrial Automation, Fault Detection, Artificial Intelligence, Predictive Maintenance, SCADA Systems, Industrial Internet of Things (IIoT), Network Resilience, Machine Learning, Real-Time Monitoring

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    S. B. Reddy and A. Verma, “Self-Healing Industrial Networks Using AI-Enabled Fault Detection”, IJMLPA, vol. 4, no. 2, pp. 01–09, Dec. 2021, doi: 10.67228/3142788X/IJMLPA-2021PII9P2D.

  • Abstract

    In the era of Industry 4.0, industrial networks are the backbone of smart manufacturing, automation, and real-time process control. Ensuring their resilience is critical for minimizing downtime and maintaining operational continuity. This paper proposes a self-healing framework for industrial networks using AI-enabled fault detection mechanisms. Leveraging machine learning and deep learning techniques, the system detects anomalies, diagnoses root causes, and initiates autonomous recovery processes. The proposed model is designed to integrate with existing SCADA systems and industrial communication protocols such as Modbus, PROFINET, and Ethernet/IP. Experimental results on simulated and real-world network data demonstrate the system's ability to detect faults with high accuracy and execute healing actions with minimal latency. The findings indicate that AI-powered fault management significantly enhances the reliability and robustness of industrial networks, paving the way toward fully autonomous and resilient industrial infrastructure.

  • References

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