Print Email Facebook Twitter Identifying and Characterizing Conveyor Belt Longitudinal Rip by 3D Point Cloud Processing Title Identifying and Characterizing Conveyor Belt Longitudinal Rip by 3D Point Cloud Processing Author Xu, Shichang (China University of Mining and Technology) Cheng, Gang (China University of Mining and Technology; Shandong Zhongheng Optoelectronic Technology Co.) Pang, Y. (TU Delft Transport Engineering and Logistics) Jin, Zujin (China University of Mining and Technology) Kang, Bin (China University of Mining and Technology) Date 2021 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. Subject longitudinal rip3D point cloudclustering processprincipal component analysis (PCA) To reference this document use: http://resolver.tudelft.nl/uuid:46a8d615-dd88-4a9f-8fed-88111880dbdc DOI https://doi.org/10.3390/s21196650 ISSN 1424-8220 Source Sensors, 21 (19) Part of collection Institutional Repository Document type journal article Rights © 2021 Shichang Xu, Gang Cheng, Y. Pang, Zujin Jin, Bin Kang Files PDF sensors_21_06650.pdf 5.08 MB Close viewer /islandora/object/uuid:46a8d615-dd88-4a9f-8fed-88111880dbdc/datastream/OBJ/view