Conveyor belt tear detection is a very important part of coal mine safety production. In this paper, a new method of detecting conveyor belt damage named audio-visual fusion (AVF) detection method is proposed. The AVF method uses both a visible light CCD and a microphone array to collect images and sounds of the conveyor belt in different running states. By processing and analyzing the collected images and sounds, the image and sound features of normal, tear and scratch can be extracted respectively. Then the extracted features of images and sounds are fused and classified by machine learning algorithm. The results show that the accuracy of AVF method for conveyor belt scratch is 93.66%, and the accuracy of longitudinal tear is higher than 96.23%. Compared with existing methods AVF method overcomes the limitation of visual detection condition, and is more accurate and reliable for conveyor belt tear detection.

输送带撕裂检测是煤矿安全生产的重要组成部分。本文提出了一种检测输送带损坏的新方法，称为视听融合（AVF）检测方法。AVF方法同时使用可见光CCD和麦克风阵列来收集处于不同运行状态的传送带的图像和声音。通过处理和分析收集的图像和声音，可以分别提取正常，撕裂和划痕的图像和声音特征。然后通过机器学习算法对提取的图像和声音特征进行融合和分类。结果表明，AVF方法对输送带刮擦的准确度为93.66％，纵向撕裂的准确度高于96.23％。与现有方法相比，AVF方法克服了视觉检测条件的局限性，