Machine Vision Inspection Technique for Conveyor Belt Deviation
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Machine Vision Inspection Technique for Conveyor Belt Deviation
输送带跑偏故障的机器视觉检测技术
Vol. 33, Issue 6, Pages: 667-671(2012)
作者机构:
天津工业大学电子与信息工程学院
作者简介:
基金信息:
DOI:
CLC:
Published:2012
稿件说明:
移动端阅览
杨彦利, 苗长云, 亢伉, et al. Machine Vision Inspection Technique for Conveyor Belt Deviation[J]. 2012, 33(6): 667-671.
DOI:
杨彦利, 苗长云, 亢伉, et al. Machine Vision Inspection Technique for Conveyor Belt Deviation[J]. 2012, 33(6): 667-671.DOI:
Machine Vision Inspection Technique for Conveyor Belt Deviation
摘要
针对矿用输送机运行过程中易发生跑偏的工程背景
研究了基于机器视觉的输送带跑偏故障检测.采用线阵CCD相机实时采集输送带图像
经二值化处理后
从二值图像中提取故障特征来识别跑偏故障.着重分析了输送带图像的快速处理和故障识别方法
提出了一种灰度平均法用于输送带与背景的快速分割;通过将二值图像映射为一维故障特征函数
设计了由跑偏角和偏移量构成的跑偏特征向量用于跑偏检测.开发了一个输送带跑偏故障视觉在线检测系统
实验室测试结果验证了该监测系统的有效性.
Abstract
Under the background that mining conveyor belts are prone to deviation in operation
a belt deviation detection technique based on machine vision was investigated.The digital images were captured through a line CCD camera.The fault of belt deviation was identified by using features extracted from binary images after binary processing.The fast image processing method and the fault identification method were analyzed in detail.The average graying method was proposed as a fast image segmentation algorithm and the deviation feature vector composed by deviation degrees and by offsets was designed to inspect belt deviation through mapping the binary image to a fault eigenfunction.An on-line vision inspection system for conveyor belt deviation detection was developed.The laboratory testing results testify the validity of the inspection system.
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references
刘持平,卢世坤.输送带跑偏原因、对策和纠偏技术的发展[J].煤矿机械,2009,30(3):7-9.Liu Chiping,Lu Shikun.Conveyor deviation ofreasons for new research on corrective measures andtechnological development outlook[J].Coal MineMachinery,2009,30(3):7-9.(in Chinese)
付守奎,杜鑫基.带式输送机输送带的纠偏方法[J].煤炭技术,2006,25(5):20-22.Fu Shoukui,Du Xinji.Correct an error ways ofconveyor’s belt[J].Coal Technology,2006,25(5):20-22.(in Chinese)
祁隽燕.基于数字图像处理的皮带纵向撕裂视觉识别[J].煤炭技术,2006,25(11):15-17.Qi Junyan.Vision identification for longitudinal rip ofsteel-cord belt based on digital image process[J].CoalTechnology,2006,25(11):15-17.(in Chinese)
祁隽燕.基于LabVIEW的胶带撕裂机器视觉检测系统研究[J].煤炭工程,2009(9):123-125.Qi Junyan.Machine vision inspection system of belttearing base on LabVIEW[J].Coal Engineering,2009(9):123-125.(in Chinese)
魏涛.煤炭输送带裂纹检测技术研究[D].太原:中北大学,2010:48-49.
滕永青.基于机器视觉和虚拟仪器技术的传送带监测系统[J].煤矿机械,2008,29(4):125-127.Teng Yongqing.Inspection system for steel-cord beltbased on machine vision and virtual instrument[J].Coal Mine Machinery,2008,29(4):125-127.(in Chinese)
魏涛.煤炭输送带表面裂纹自动检测技术研究[J].煤矿机械,2010,31(2):211-213.Wei Tao.Research on automatic detection technologyfor coal conveyor belt surface crack[J].Coal MineMachinery,2010,31(2):211-213.(in Chinese)
Fromme C C.Conveyor belt inspection system andmethod:International Patent,WO03059789A2[P].2003-07-24.
Zhu A C,Hua G.The research on the detectionmethod of belt deviation by video in coal mine[C].International Conference on Mechatronic Science,Electric Engineering and Computer,2011:430-433.
Sun Z H.On-road vehicle detection:A review[J].IEEE Transactions on Pattern Analysis and MachineIntelligence,2006,28(5):694-711.
Tessier J,Duchesne C,Bartolacci G.A machinevision approach to on-line estimation of run-of-mineore composition on conveyor belts[J].MineralsEngineering,2007,20(12):1129-1144.
Li Z Y,Liu C C,Liu G H,et al.A novel statisticalimage thresholding method[J].Int.J.Electron.Commun.,2010,64(12):1137-1147.
Carnicer R M.A novel histogram transformation toimprove the performance of thresholding methods inedge detection[J].Pattern Recognition Lett.,2011,32(5):676-693.
李宏.基于Otsu理论的灰度图像分割算法研究和改进[J].科学技术与工程,2010,10(22):5437-5440.Li Hong.The study of gray-level image segmentationalgorithm based on Otsu method and its improvement[J].Science Technology and Engineering,2010,10(22):5437-5440.(in Chinese)