ABSTRACT

Belt conveyors are important transportation equipment in coal mining enterprises. At present, most research on this topic focuses on areas such as tear resistance and foreign body identification. Few studies have focused on belt wear, but belt wear is the subject of daily inspections on site. The artificial grayscale analysis method, support vector machine (SVM) method, and deep learning network are proposed herein to identify the degree of belt wear by using image acquisition devices installed on belt conveyors to collect images of no-load belts, instead of manual inspection. The experimental results indicate that the grayscale analysis method has limitations in identifying belt wear. For complex types of wear, such as annular wear, its recognition capability is poor, and the grayscale analysis method is heavily dependent on the results of human analysis. The highest accuracy of the SVM method is 84.5%, and it effectively identifies complex wear states. After training, worn belts can be detected automatically. However, the selection of features during training completely depends on human decisions, and the accuracy is affected by such factors that have a great influence. The deep learning network attained a 91.5% average recognition accuracy rate with the highest accuracy being 95%. It can fully automate intelligent feature selection, training and detection.

摘要

带式输送机是煤矿企业重要的输送设备。目前，该课题的研究大多集中在抗撕裂和异物识别等领域。很少有研究关注皮带磨损，但皮带磨损是现场日常检查的主题。本文提出人工灰度分析方法、支持向量机（SVM）方法和深度学习网络，利用安装在带式输送机上的图像采集设备采集空载皮带图像，代替人工检测来识别皮带磨损程度. 实验结果表明，灰度分析方法在识别皮带磨损方面存在局限性。对于复杂的磨损类型，如环形磨损，其识别能力较差，并且灰度分析方法严重依赖于人为分析的结果。SVM方法的准确率最高可达84.5%，能够有效识别复杂的磨损状态。训练结束后，可以自动检测磨损的皮带。然而，训练时特征的选择完全取决于人的决定，准确率受此类影响很大的因素影响。深度学习网络达到了91.5%的平均识别准确率，最高准确率为95%。它可以完全自动化智能特征选择、训练和检测。而精度受这些因素影响很大。深度学习网络达到了91.5%的平均识别准确率，最高准确率为95%。它可以完全自动化智能特征选择、训练和检测。而精度受这些因素影响很大。深度学习网络达到了91.5%的平均识别准确率，最高准确率为95%。它可以完全自动化智能特征选择、训练和检测。