Intelligent Sensor Fault Diagnosis in Autonomous Robotic Platforms

  • Authors

    • Dr. James Carter Professor, University of Texas at Austin, USA Author
    • Dr. Patricia Hall Associate Professor, University of Michigan, USA Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2022PI4G5L

    Published 02-04-2022

  • Intelligent Sensor Fault Diagnosis, Autonomous Robots, Artificial Intelligence, Deep Learning, Predictive Maintenance, Multi-Sensor Fusion, Industrial Robotics, Edge Intelligence

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    J. Carter and P. Hall, “Intelligent Sensor Fault Diagnosis in Autonomous Robotic Platforms”, IJIARE, vol. 5, no. 1, pp. 01–07, Feb. 2022, doi: 10.67228/30715725/IJIARE-2022PI4G5L.

  • Abstract

    Autonomous robotic platforms have become an integral part of industrial automation, intelligent manufacturing, autonomous transportation, precision agriculture, healthcare robotics, and hazardous environment exploration. The operational reliability of these robotic systems strongly depends on the accuracy and health of their sensing infrastructure. Sensors including inertial measurement units (IMUs), LiDAR, cameras, ultrasonic sensors, GPS modules, encoders, force-torque sensors, and proximity sensors continuously provide environmental and operational information for autonomous decision-making. However, sensor degradation, calibration drift, communication failures, environmental interference, and hardware aging can significantly deteriorate robotic performance and may even lead to catastrophic failures. Consequently, intelligent sensor fault diagnosis has emerged as a critical research area that combines artificial intelligence, machine learning, data analytics, and model-based reasoning to identify, classify, and predict sensor faults in real time. In this paper, a complete intelligent sensor fault diagnosis framework for autonomous robotic platforms is proposed. The proposed framework utilizes the integration of multi-sensor data fusion, feature extraction, deep learning-based fault classification, anomaly detection, and predictive maintenance components into a unified architecture. Experimental performance evaluation shows significant enhancements versus traditional threshold-based diagnostic approaches in terms of high fault detection accuracy, lower false alarms and reduction in the latency for diagnosis among different faults with an increased reliability level on system diagnosis. These results demonstrate that AI-assisted diagnostic models significantly increased robotic autonomy through the ability to proactively manage faults, reduce downtime and enhance operational safety. The proposed framework is suitable for deployment in Industry 5.0 manufacturing systems, autonomous vehicles, collaborative robots, and intelligent service robotics.

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