Xi’an Hertz Universe Information Technology Co., Ltd.

为了防止煤炭开采运输过程中的异物对运输设备和生产设备产生损坏，结合传统的带式输送机检测系统研制了一种基于机器视觉深度学习的带式输送机故障辅助识别系统；通过图像算法库进行图像预处理，增强系统对有关信息的可检测性；使用深度学习训练得出的识别网络模型利用监控视频对异物进行识别，提高系统识别异物的准确率，有效提高运输环节的运输效率。试验结果表明：故障辅助识别系统可以保证综采工作面运输系统的正常运行。

In order to prevent the foreign matters in the process of coal mining and transportation from damaging the transportation equipment and production equipment, it is proposed to research a belt conveyor fault auxiliary recognition system based on machine vision deep learning in combination with the traditional belt conveyor detection system. Image preprocessing is carried out through the image algorithm library to enhance the detectability of the system for relevant information; the recognition network model obtained by in-depth learning training uses monitoring video to identify foreign objects, improve the accuracy of the system to identify foreign objects, and effectively improve the transport efficiency of the transport link. The test results indicate that the fault auxiliary identification system can ensure the normal operation of the transportation system in the fully mechanized mining face.

李旭，董博，党恩辉. 矿用带式输送机故障辅助识别系统实证研究[J]. 煤矿安全，2023，54（9）：208−211

LI Xu, DONG Bo, DANG Enhui. Empirical study on fault aided identification system of mine belt conveyor[J]. Safety in Coal Mines, 2023, 54（9）: 208−211