208convSateinCoalMines2023,54(9):208-211.移动扫码阅读LIXu,DONGBo,DANGEnhui.Empiricalstudyonfaultaidedidentificationsystemofminebelt54(9):208-211.李旭，重输送机故障辅助识别系统实证研究安全，2023SafetyinCoalMinesSep.20232023年9月No.9Vol1.54煤砺发全第9期第54卷DOI:10.13347/j.cnki.mkaq.2023.09.028矿用带式输送机故障辅助识别系统实证研究李旭，董博，党恩辉（西安合智宇信息科技有限公司，陕西西安710075）摘要：为了防止煤炭开采运输过程中的异物对运输设备和生产设备产生损坏，结合传统的带式输送机检测系统研制了一种基于机器视觉深度学习的带式输送机故障辅助识别系统；通过图像算法库进行图像预处理，增强系统对有关信息的可检测性；使用深度学习训练得出的识别网络模型利用监控视频对异物进行识别，提高系统识别异物的准确率，有效提高运输环节的运输效率。试验结果表明：故障辅助识别系统可以保证综采工作面运输系统的正常运行。关键词：带式输送机；故障识别；异物监测；监控系统；计算机视觉中图分类号：TD679文献标志码：B文章编号：1003-496X（2023）09-0208-04EmpiricalstudyonfaultaidedidentificationsystemofminebeltconveyorLIXu,DONGBo,DANGEnhui(Xi'anHertzUniverseInformationTechnologyCo.,Ltd.,Xi'an710075,China)Abstract:Inordertopreventtheforeignmattersintheprocessofcoalminingandtransportationfromdamagingthetransportationequipmentandproductionequipment,itisproposedtoresearchabeltconveyorfaultauxiliaryrecognitionsystembasedonmachinevisiondeeplearningincombinationwiththetraditionalbeltconveyordetectionsystem.Imagepreprocessingiscarriedoutthroughtheimagealgorithmlibrarytoenhancethedetectabilityofthesystemforrelevantinformation;therecognitionnetworkmodelob-tainedbyin-depthlearningtrainingusesmonitoringvideotoidentifyforeignobjects,improvetheaccuracyofthesystemtoidentifyforeignobjects,andeffectivelyimprovethetransportefficiencyofthetransportlink.Thetestresultsindicatethatthefaultauxiliaryidentificationsystemcanensurethenormaloperationofthetransportationsysteminthefullymechanizedminingface.Keywords:beltconveyor;faultidentification;foreignbodyidentification;monitoringsystem;computervision近年来，伴随矿井生产能力、开采深度的...
