Longitudinal tear detection method of conveyor belt based on audio-visual fusion

Jian Che, Tiezhu Qiao, Yi Yang, Haitao Zhang, Yusong Pang

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.

Original languageEnglish
Article number109152
Number of pages12
JournalMeasurement: Journal of the International Measurement Confederation
Volume176
DOIs
Publication statusPublished - 2021

Keywords

  • Audio-visual feature extraction
  • Feature fusion
  • Longitudinal tear detection method
  • Machine learning

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