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基于热红外图像的带式输送机托辊故障检测研究

郭砚秋 苗长云 刘意

郭砚秋,苗长云,刘意. 基于热红外图像的带式输送机托辊故障检测研究[J]. 工矿自动化,2023,49(10):52-60.  doi: 10.13272/j.issn.1671-251x.2022120051
引用本文: 郭砚秋,苗长云,刘意. 基于热红外图像的带式输送机托辊故障检测研究[J]. 工矿自动化,2023,49(10):52-60.  doi: 10.13272/j.issn.1671-251x.2022120051
GUO Yanqiu, MIAO Changyun, LIU Yi. Research on fault detection of belt conveyor roller based on thermal infrared image[J]. Journal of Mine Automation,2023,49(10):52-60.  doi: 10.13272/j.issn.1671-251x.2022120051
Citation: GUO Yanqiu, MIAO Changyun, LIU Yi. Research on fault detection of belt conveyor roller based on thermal infrared image[J]. Journal of Mine Automation,2023,49(10):52-60.  doi: 10.13272/j.issn.1671-251x.2022120051

基于热红外图像的带式输送机托辊故障检测研究

doi: 10.13272/j.issn.1671-251x.2022120051
基金项目: 国家自然科学基金面上项目(NSFC51274150);天津市重点研发计划科技支撑项目(18YFZCGX00930)。
详细信息
    作者简介:

    郭砚秋 (1995— ),女,山西大同人,硕士,主要研究方向为电子信息,E-mail:gyqzzz123@163.com

  • 中图分类号: TD634.1

Research on fault detection of belt conveyor roller based on thermal infrared image

  • 摘要: 针对目前带式输送机巡检机器人搭载红外采集设备移动受限,存在不能实时进行数据采集、数据处理并上传至监控终端,无法完成远程故障检测,续航能力不足等问题,提出了一种基于热红外图像的带式输送机托辊故障检测方法。带式输送机巡检机器人搭载托辊故障检测器及红外热像仪,红外热像仪将采集的托辊热红外图像序列与温度数据传输给托辊故障检测器进行托辊故障检测,检测结果由托辊故障检测器内置的WH−L101无线传输模块发送给上位机。提出了一种带式输送机托辊故障检测算法:利用YOLOv5s目标检测算法提取托辊热红外图像的感兴趣区域(ROI),采用维纳滤波和自适应中值滤波算法对ROI图像进行滤波,利用自适应直方图均衡化和图像锐化算法对滤波后的ROI图像进行增强,采用基于形态学的Otsu图像分割算法对增强后的ROI图像进行图像分割,得到待检测的托辊图像,利用Harris角点检测算法提取托辊图像特征,获得托辊位置信息,提取相应位置的温度信息,并采用基于相对温差法的托辊故障检测算法判定托辊故障。实验结果表明:① YOLOv5s网络模型提取托辊ROI的目标检测结果平均准确率为99.12%。② 提出的托辊故障检测算法对托辊故障(无故障、轴承锈蚀、托辊卡转、筒体磨穿)检测的平均准确率为97.625%,帧率为16 帧/s。③ 将检测结果通过无线传输模块传送至上位机,可显示故障类型及关键区域温度,并进行报警。

     

  • 图  1  基于热红外图像带式输送机托辊故障检测系统

    Figure  1.  Fault detection system of belt conveyor roller based on thermal infrared image

    图  2  托辊故障检测算法流程

    Figure  2.  Process of the roller fault detection algorithm

    图  3  YOLOv5s网络模型处理结果

    Figure  3.  YOLOv5s network model processing results

    图  4  维纳滤波结果

    Figure  4.  Wiener filter results

    图  5  自适应中值滤波结果

    Figure  5.  Adaptive median filtering results

    图  6  图像增强结果

    Figure  6.  Image enhancement results

    图  7  基于形态学的Otsu图像分割算法处理结果

    Figure  7.  Image processing results by morphology-based Otsu image segmentation algorithm

    图  8  基于Harris角点检测算法的特征提取结果

    Figure  8.  Feature extraction results by Harris corner point detection algorithm

    图  9  带式输送机托辊故障检测器硬件组成

    Figure  9.  Hardware composition of fault detector for belt conveyor roller

    图  10  托辊故障检测器软件流程

    Figure  10.  Flow of idler fault detector software

    图  11  带式输送机托辊故障检测实验平台

    Figure  11.  Experimental platform for belt conveyor idler fault detection

    图  12  YOLOv5s目标检测结果

    Figure  12.  YOLOv5s target detection results

    图  13  托辊故障

    Figure  13.  The roller fault

    图  14  4种状态下TA区域最大温度及BA区域平均温度

    Figure  14.  The maximum temperature in TA region and average temperature in BA region under 4 states

    图  15  托辊故障检测结果

    Figure  15.  Roller fault detection results

    表  1  网络训练和测试平台配置

    Table  1.   Network training and test platform configuration

    设备参数
    CPUIntel(R) Xeon(R) CPU E5−2678
    GPUNvidia Geforce GTX1080Ti
    内存64 GiB DDR4
    操作系统64位Ubuntu 18.04LTS
    深度学习框架Pytorch
    下载: 导出CSV

    表  2  托辊故障判定标准

    Table  2.   Roller fault judgment criteria

    指标轴承锈蚀故障(B1)托辊卡转故障(C1)筒体磨穿故障(D1)
    相对温差/%76.7≤$ \alpha $<82.282.2≤$ \alpha $<92.3$ \alpha $≥92.3
    下载: 导出CSV

    表  3  托辊故障检测正确数量

    Table  3.   Correct number of roller fault detection

    托辊A0B1C1D1
    托辊199969898
    托辊2100979697
    总正确量199193194195
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-16
  • 修回日期:  2023-10-09
  • 网络出版日期:  2023-10-23

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