Research on Audio-Visual Detection Method for Conveyor Belt Longitudinal Tear

• Published in: IEEE access Vol. 7; pp. 120202 - 120213
• Main Authors: Hou, Chengcheng, Qiao, Tiezhu, Qiao, Meiying, Xiong, Xiaoyan, Yang, Yi, Zhang, Haitao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Audio visual equipment; Audio-visual detection method; Belt conveyors; Belts; Cameras; Coal mines; Coal mining; conveyor belt; Image segmentation; Knee; longitudinal tear; Microphone arrays; Mining industry; Sound; Tearing
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2937660

As a key equipment for production and transportation, conveyor belts are widely used in the coal mining industry. Once the conveyor belt longitudinal tear occurs, it will seriously affect the production and even cause personal injury. Therefore, the longitudinal tear detection of the conveyor belt is extremely important. In this paper, the sound detection is introduced into the longitudinal tear detection of the conveyor belt, and an audio-visual detection method for conveyor belt longitudinal tear is proposed. Camera and microphone array are used to collect the image and sound signals of conveyor belt, and the conveyor belt is detected from both image and sound, respectively. Then the image and sound analysis results are combined to comprehensively judge the status of the conveyor belt. The experimental results show that the audio-visual detection method can accurately identify the normal, abnormal, and longitudinal tear of the conveyor belt. The detection accuracy is over 86.72% and the sensitivity of longitudinal tear detection is greater than 92.59%. The proposed audio-visual detection method is verified to meet the requirements of longitudinal tear detection of coal mining industry conveyor belts.