AI-powered health monitoring of anode baking furnace pits in aluminum production using autonomous drones

Abstract

Industrial health monitoring in factories is essential for quality assurance, energy and cost reduction, and health and safety. In aluminum factories, anode furnace pits’ flue walls deform over time due to cyclic heating and cooling. They are inspected and classified using manually acquired measurements in a process that takes several hours and is done under high temperatures using specialized equipment. We propose an end-to-end AI-powered system for automated inspection using drones. We fly a drone carrying color and depth cameras to film and navigate a 50-pit furnace floor autonomously in a GPS-denied environment. We then mosaic the recorded videos to produce color and depth mosaics using frame-to-frame motion parameters estimated using the color videos and applied to both. We finish the mosaics using depth-to-color mosaic registration based on maximizing mutual information on gradients. We extract pit images from the mosaics using a YOLOv5 object detection initially trained using a physical floor model with a proposed data augmentation scheme and then fine-tuned for the on-site environment. We achieve a mean average precision of 94.7%. Once pit images are detected and initially classified, we propose a dual-stage segmentation algorithm using the Hough transform and a semantic segmentation network trained using probabilistic feature images with a precision of 96.67%. Segmented pits with depth information allow us to produce 3D models of pits to aid in temporal monitoring and diagnosis confirmation. Our system is cost-effective and reduces inspection downtime by 87%, eliminating the need for human intervention.