Cognitive Digital Twins with Continual Learning for Industry 4.0 Systems

  • Authors

    • Dr. Harish Patel Professor, Gujarat University, India. Author
    • Dr. Geetha Ramasamy Associate Professor, Madurai Kamaraj University, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2018PII3V7R

    Published 10-05-2018

  • Cognitive Digital Twin, Continual Learning, Industry 4.0, Adaptive Systems, Catastrophic Forgetting, Cyber-Physical Systems, Smart Manufacturing, Industrial AI, Edge Intelligence, Human-Machine Collaboration

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    H. Patel and G. Ramasamy, “Cognitive Digital Twins with Continual Learning for Industry 4.0 Systems”, IJMLPA, vol. 1, no. 2, pp. 01–11, Oct. 2018, doi: 10.67228/3142788X/IJMLPA-2018PII3V7R.

  • Abstract

    The evolution of Industry 4.0 has driven the adoption of intelligent and autonomous systems capable of adapting to dynamic environments. Among these, Digital Twins (DTs) have emerged as virtual replicas of physical systems that enable real-time monitoring, simulation, and optimization. However, traditional DTs often suffer from static behavior and lack adaptive intelligence, limiting their utility in non-stationary industrial environments. This paper introduces the concept of Cognitive Digital Twins (CDTs) augmented with Continual Learning (CL) capabilities to overcome these limitations. By integrating cognitive architectures and continual learning mechanisms, CDTs are able to learn from streaming data, retain previously acquired knowledge, and adapt to evolving industrial processes without catastrophic forgetting. The paper presents a conceptual framework for CDTs with CL, explores key enabling technologies, and provides use-case scenarios in predictive maintenance, process optimization, and human-machine collaboration. Challenges and research directions for deploying CDTs in real-world Industry 4.0 ecosystems are also discussed.

  • References

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