| 基于机器视觉的列车部件故障诊断方法综述 |
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| 引用本文： | 刘子仪,李兆新,陈建强,李书盼,陆其波,宋威宏.基于机器视觉的列车部件故障诊断方法综述[J].计算机应用与软件,2022,39(1):1-9,52. |
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| 作者姓名： | 刘子仪 李兆新 陈建强 李书盼 陆其波 宋威宏 |
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| 作者单位： | 西南交通大学电气工程学院 四川 成都 611756;华南理工大学电子与信息学院 广东 广州 510500;广州地铁集团有限公司 广东 广州 510310;广州地铁集团有限公司 广东 广州 510310 |
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| 基金项目： | 国家自然科学基金项目(U1934221,61773323,61733015);四川省科技厅基金项目(2019YJ0210,2019YFG0345);山东省安全控制技术重点实验室开放课题(SKDN202004)。 |
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| 摘 要： | 基于机器视觉的列车部件故障诊断方法提高了人工巡检的速度和准确率,是近年来机器视觉技术的研究热点之一.通过对机器视觉技术在列车故障诊断方面的研究成果的回顾,对其中的图像配准和故障诊断等关键技术进行综述,总结相关原理、优缺点、国内外发展现状.3D技术的发展为列车故障诊断提供了新的研究方向.对3D检测技术的原理及其在列车部件...
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| 关 键 词： | 机器视觉 列车部件故障诊断 图像配准 故障检测 3D 检测 |
A SURVEY OF FAULT DIAGNOSIS OF TRAIN COMPONENTS BASED ON MACHINE VISION |
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| Liu Ziyi,Li Zhaoxin,Chen Jianqiang,Li Shupan,Lu Qibo,Song Weihong.A SURVEY OF FAULT DIAGNOSIS OF TRAIN COMPONENTS BASED ON MACHINE VISION[J].Computer Applications and Software,2022,39(1):1-9,52. |
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| Authors: | Liu Ziyi Li Zhaoxin Chen Jianqiang Li Shupan Lu Qibo Song Weihong |
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| Affiliation: | (School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,Sichuan,China;School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510500,Guangdong,China;Guangzhou Metro Group Co.,Ltd.,Guangzhou 510310,Guangdong,China) |
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| Abstract: | Machine-vision-based fault diagnosis methods of train components improve the speed and accuracy of manual inspection remarkably,and have gradually become a research hotspot in recent years.Aiming at the key steps of fault diagnosis of train components via machine vision,advanced technologies of image registration and fault diagnosis of key components were summarized in this paper.The principle,advantages and disadvantages of typical image registration and fault diagnosis algorithms,as well as the development status at home and abroad,were presented.Then,considering the development of 3D technology,the paper briefly introduced the principle of 3D detection and the research status of fault diagnosis of train components using 3D technology.Finally,the existing problems of various methods of fault diagnosis of train components and the future development trend were presented. |
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| Keywords: | Machine vision Fault diagnosis of train components Image registration Failure recognition 3D Detection |
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