Identifying and Characterizing Conveyor Belt Longitudinal Rip by 3D Point Cloud Processing
Shichang Xu (China University of Mining and Technology)
Gang Cheng ( Shandong Zhongheng Optoelectronic Technology Co., China University of Mining and Technology)
Y Pang (TU Delft - Transport Engineering and Logistics)
Zujin Jin (China University of Mining and Technology)
Bin Kang (China University of Mining and Technology)
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Abstract
Real-time and accurate longitudinal rip detection of a conveyor belt is crucial for the safety and efficiency of an industrial haulage system. However, the existing longitudinal detection methods possess drawbacks, often resulting in false alarms caused by tiny scratches on the belt surface. A method of identifying the longitudinal rip through three-dimensional (3D) point cloud processing is proposed to solve this issue. Specifically, the spatial point data of the belt surface are acquired by a binocular line laser stereo vision camera. Within these data, the suspected points induced by the rips and scratches were extracted. Subsequently, a clustering and discrimination mechanism was employed to distinguish the rips and scratches, and only the rip information was used as alarm criterion. Finally, the direction and maximum width of the rip can be effectively characterized in 3D space using the principal component analysis (PCA) method. This method was tested in practical experiments, and the experimental results indicate that this method can identify the longitudinal rip accurately in real time and simultaneously characterize it. Thus, applying this method can provide a more effective and appropriate solution to the identification scenes of longitudinal rip and other similar defects.
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