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

Abstract

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.

Design of IoT Gateway for Crop Growth Environmental Monitoring Based on Edge-Computing Technology

“…IoT gateway is the core component, and it is the bridge between the terminal nodes and the cloud. is paper designs an edge-computing gateway, and it is the regional network information center of crop growth environmental [5] Finding nodes of Internet of things ✓ [6] Traceability of shipyard pipeline processing ✓ [7] Detection of industrial thermal anomaly ✓ [8] Manufacturing architecture of decision-making ✓ ✓ [9] Encryption of smart grid data ✓ [10] Networked control of industrial robots ✓ ✓ [11] Application of machine learning in industry ✓ ✓ [12] A smart IoT monitoring based on UAV ✓ ✓ 2 Computational Intelligence and Neuroscience monitoring IoT.…”

A Model for Working Environment Monitoring in Smart Manufacturing

“…Ghazal et al [21] proposed an edge-fog-cloud architecture for detecting thermal anomalies in aluminum factories. Sensing was performed with mobile IoT edge nodes carried on autonomous robots, while using drones as fog nodes increased the responsiveness of the system and reduced surveying costs.…”

“…In addition, the use of contemporary technologies, such as body area networks, cloud computing, and smart clothing, big data, and business intelligence, or embedded and pervasive computing, allow the improvement of working condition monitoring and the quality of health services within manufacturing environments [10]. The use of sensor-based systems with fixed and mobile sensors for monitoring in industrial environments means the need for human involvement in monitoring is reduced, which minimizes the possibility of getting imprecise monitoring data and the exposure of workers to additional risks [21].…”

Progress on Infrared Imaging Technology in Animal Production: A Review

“…In addition, IRT is real-time, noninvasive, and does not expose living organisms to strong, harmful radiation like x-rays [ 20 ]. Over the past few years, IRT has been widely used in the fields of medicine [ 21 , 22 , 23 ], agriculture [ 24 , 25 , 26 , 27 , 28 ], architecture [ 29 , 30 ], energy [ 31 ], and aviation [ 32 , 33 ]. IRT imaging technology has also been applied to animal production [ 34 ].…”