Predictive AI Models for Intelligent Robot Health Monitoring

  • Authors

    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2024PII1Z8N

    Published 08-04-2024

  • Predictive Artificial Intelligence, Intelligent Robot Health Monitoring, Predictive Maintenance, Machine Learning, Deep Learning, Remaining Useful Life (RUL), Fault Diagnosis, Industrial Robotics, Condition Monitoring, Industry 4.0, Intelligent Manufacturing, Sensor Fusion

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    M. H. N, “Predictive AI Models for Intelligent Robot Health Monitoring”, IJIARE, vol. 7, no. 2, pp. 01–16, Aug. 2024, doi: 10.67228/30715725/IJIARE-2024PII1Z8N.

  • Abstract

    Intelligent robotic systems increasingly require predictive health monitoring to ensure reliability, safety, and operational efficiency. Traditional maintenance methods cannot accurately predict failures or estimate component lifespan. This paper proposes an AI-driven predictive health monitoring framework that integrates multi-sensor data, intelligent feature engineering, deep learning, anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) estimation. Using data from vibration, temperature, motor current, torque, acoustic signals, batteries, and controller logs, the framework continuously assesses robot health and predicts failures in real time. It supports intelligent maintenance scheduling, resource optimization, and autonomous maintenance decisions through adaptive learning. The proposed approach improves fault detection accuracy, reduces downtime and maintenance costs, enhances robot reliability and productivity, and supports the development of self-aware robotic systems for Industry 4.0 and Industry 5.0 smart manufacturing environments.

  • References

    [1] A. Carvalho, P. Leitão, and E. Tovar, “Artificial intelligence for predictive maintenance in Industry 4.0: A systematic literature review,” IEEE Access, vol. 9, pp. 162694–162714, 2021.

    [2] S. Zhang, Y. Wang, X. Li, and H. Liu, “Deep learning-based intelligent fault diagnosis for industrial robots using multi-sensor fusion,” IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5415–5425, Aug. 2022.

    [3] Y. Lei, N. Li, L. Guo, N. Li, T. Yan, and J. Lin, “Machine learning for predictive maintenance: Recent advances and future trends,” IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 4921–4935, May 2022.

    [4] M. R. Habib, J. Lee, and K. Kim, “Explainable artificial intelligence for predictive maintenance in smart manufacturing,” IEEE Access, vol. 10, pp. 56732–56749, 2022.

    [5] X. Zhao, H. Pan, and L. Zhang, “Remaining useful life prediction of industrial robots using LSTM neural networks,” IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 1154–1166, Apr. 2023.

    [6] J. Wang, Y. Li, and M. Pecht, “Digital twin-driven predictive maintenance for industrial equipment: A review,” IEEE Access, vol. 11, pp. 19425–19448, 2023.

    [7] T. Wen, Z. Chen, and Q. Sun, “Transformer-based fault diagnosis for industrial machinery using multivariate sensor signals,” IEEE Sensors Journal, vol. 23, no. 9, pp. 10082–10094, May 2023.

    [8] H. Luo, B. Xu, and F. Tao, “AI-enabled intelligent maintenance in Industry 5.0 manufacturing systems,” IEEE Transactions on Industrial Informatics, vol. 20, no. 1, pp. 412–424, Jan. 2024.

    [9] C. Li, X. Zhou, and Y. Zhang, “Graph neural network-based predictive maintenance for industrial cyber-physical systems,” IEEE Internet of Things Journal, vol. 11, no. 4, pp. 6458–6472, Feb. 2024.

    [10] M. Singh and R. Gupta, “Hybrid deep learning framework for industrial robot fault diagnosis using vibration and thermal signals,” IEEE Access, vol. 12, pp. 55481–55497, 2024.

    [11] P. Kumar, S. Sharma, and A. Verma, “Multi-sensor data fusion for intelligent health monitoring of robotic systems,” IEEE Sensors Journal, vol. 24, no. 7, pp. 11234–11247, Apr. 2024.

  • Downloads

Similar Articles

1-10 of 84

You may also start an advanced similarity search for this article.