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Article

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things

1
Electrical and Computer Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, UAE
2
Mechanical Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, UAE
3
Emirates Global Aluminium, Technology Development and Transfer Midstream, Abu Dhabi 109111, UAE
4
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(21), 6348; https://doi.org/10.3390/s20216348
Received: 21 September 2020 / Revised: 2 November 2020 / Accepted: 3 November 2020 / Published: 7 November 2020
Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates. View Full-Text
Keywords: edge-fog-cloud computing; Internet of Things; robotics; artificial intelligence; autonomous driving; image registration edge-fog-cloud computing; Internet of Things; robotics; artificial intelligence; autonomous driving; image registration
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MDPI and ACS Style

Ghazal, M.; Basmaji, T.; Yaghi, M.; Alkhedher, M.; Mahmoud, M.; El-Baz, A.S. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors 2020, 20, 6348. https://doi.org/10.3390/s20216348

AMA Style

Ghazal M, Basmaji T, Yaghi M, Alkhedher M, Mahmoud M, El-Baz AS. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors. 2020; 20(21):6348. https://doi.org/10.3390/s20216348

Chicago/Turabian Style

Ghazal, Mohammed, Tasnim Basmaji, Maha Yaghi, Mohammad Alkhedher, Mohamed Mahmoud, and Ayman S. El-Baz 2020. "Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things" Sensors 20, no. 21: 6348. https://doi.org/10.3390/s20216348

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