Guo Xiaoqiang, Liu Xinhua, Zhou Hao, Stanislawski Rafal, Królczyk Grzegorz, Li Zhixiong
School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 211006, China.
School of Intelligent Manufacturing, Suzhou Chien-Shiung Institute of Technology, Taicang 215400, China.
Micromachines (Basel). 2022 Mar 17;13(3):449. doi: 10.3390/mi13030449.
The belt conveyor is the most commonly used conveying equipment in the coal mining industry. As the core part of the conveyor, the belt is vulnerable to various failures, such as scratches, cracks, wear and tear. Inspection and defect detection is essential for conveyor belts, both in academic research and industrial applications. In this paper, we discuss existing techniques used in industrial production and state-of-the-art theories for conveyor belt tear detection. First, the basic structure of conveyor belts is discussed and an overview of tear defect detection methods for conveyor belts is studied. Next, the causes of conveyor belt tear are classified, such as belt aging, scratches by sharp objects, abnormal load or a combination of the above reasons. Then, recent mainstream techniques and theories for conveyor belt tear detection are reviewed, and their characteristics, advantages and shortcomings are discussed. Furthermore, image dataset preparation and data imbalance problems are studied for belt defect detection. Moreover, the current challenges and opportunities for conveyor belt defect detection are discussed. Lastly, a case study was carried out to compare the detection performance of popular techniques using industrial image datasets. This paper provides professional guidelines and promising research directions for researchers and engineers based on the leading theories in machine vision and deep learning.
带式输送机是煤矿行业最常用的输送设备。作为输送机的核心部件，输送带容易出现各种故障，如划痕、裂缝、磨损等。无论是在学术研究还是工业应用中，输送带的检测和缺陷检测都至关重要。在本文中，我们讨论了工业生产中使用的现有技术以及输送带撕裂检测的最新理论。首先，讨论了输送带的基本结构，并研究了输送带撕裂缺陷检测方法的概述。其次，对输送带撕裂的原因进行了分类，如皮带老化、尖锐物体划伤、异常负载或上述原因的组合。然后，回顾了最近用于输送带撕裂检测的主流技术和理论，并讨论了它们的特点、优点和缺点。此外，还研究了用于皮带缺陷检测的图像数据集准备和数据不平衡问题。此外，还讨论了输送带缺陷检测当前面临的挑战和机遇。最后，进行了一个案例研究，以使用工业图像数据集比较流行技术的检测性能。本文基于机器视觉和深度学习的前沿理论，为研究人员和工程师提供了专业指导和有前景的研究方向。
