Design and Application of Visual Inspection System for Running Status of Belt Conveyor

Aiming at the low coal flow recognition accuracy and slow response speed of the belt conveyor system, which make it difficult to meet the requirements of coal volume-belt speed adaptive matching control, this paper proposes a novel visual inspection system scheme for conveyor operating status. The system realizes accurate coal flow monitoring based on visual inspection and deep learning methods: the visual acquisition module completes extraction, segmentation, enhancement and analysis of coal flow images, and deep learning theory is adopted to optimize the data analysis and calculation strategy for coal flow detection. Engineering application results show that the coal flow detection error of this new system can be controlled within 0.2%, which lays a foundation for realizing the adaptive adjustment of coal volume-belt speed and improving the operation economy of the conveyor.