基于深度卷积网络的中低速磁浮接触轨紧固件松动检测

李程; 陈健雄; 林军; 康高强

doi:10.13890/j.issn.1000-128X.2022.04.025

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基于深度卷积网络的中低速磁浮接触轨紧固件松动检测

检测技术 | 更新时间：2024-08-02

- 基于深度卷积网络的中低速磁浮接触轨紧固件松动检测
- Looseness detection of fasteners on conductor rails of medium and low speed maglev railways based on deep convolutional networks
- 机车电传动 2022年第4期页码：172-179
- 作者机构：
  
  1.中车株洲电力机车研究所有限公司，湖南株洲　412001
  2.中铁第四勘察设计院集团有限公司，湖北武汉;430063
- 作者简介：
  
  康高强（1986—），男，博士，工程师，主要从事轨道交通智能化方面的研究；E-mail: kanggqyyy@163.com
- 基金信息：
  
  湖湘英才项目(2020RC3095)
- DOI：10.13890/j.issn.1000-128X.2022.04.025
  中图分类号： U237
- 纸质出版日期：2022-07-10，
  
  收稿日期：2022-01-17，
  
  修回日期：2022-03-03，
- 稿件说明：
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李程, 陈健雄, 林军, 等. 基于深度卷积网络的中低速磁浮接触轨紧固件松动检测[J]. 机车电传动, 2022,(4):172-179.

LI Cheng, CHEN Jianxiong, LIN Jun, et al. Looseness detection of fasteners on conductor rails of medium and low speed maglev railways based on deep convolutional networks[J]. Electric drive for locomotives, 2022,(4):172-179.
李程, 陈健雄, 林军, 等. 基于深度卷积网络的中低速磁浮接触轨紧固件松动检测[J]. 机车电传动, 2022,(4):172-179. DOI： 10.13890/j.issn.1000-128X.2022.04.025.

LI Cheng, CHEN Jianxiong, LIN Jun, et al. Looseness detection of fasteners on conductor rails of medium and low speed maglev railways based on deep convolutional networks[J]. Electric drive for locomotives, 2022,(4):172-179. DOI： 10.13890/j.issn.1000-128X.2022.04.025.

摘要

针对中低速磁浮接触轨紧固件松动的问题，提出一种基于卷积神经网络的检测算法。该算法对底座安装螺栓和连接板螺钉2种紧固件进行松动检测：首先定位接触轨紧固件所在区域，以此排除背景干扰；然后分析紧固件位置变动情况，实现接触轨紧固件的松动检测。通过深度卷积网络对该算法进行了实现和试验验证：采用YOLO V2网络定位2种紧固件所在区域；利用Mask R-CNN网络同时对连接板边缘、绝缘子、螺栓和螺杆，以及连接板螺钉的头部进行分割；通过对分割部位的位置变动情况进行判断实现对紧固件的松动检测。使用长沙中低速磁浮接触轨数据对本文提出的缺陷检测算法进行了试验，底座安装螺栓和连接板螺钉松动检测的精确率均在90%以上，召回率在94%以上。试验结果表明，本文所提的方法能准确地识别出中低速磁浮接触轨松动的紧固件。

Abstract

To deal with the looseness of fasteners on the conductor rails of the medium and low speed maglev railways

a detection algorithm based on deep convolutional networks was proposed for loose detection of two kinds of fasteners

i.e. the base mounting bolt and the connecting plate screw. The first step was to locate the fastener area on the conductor rail for the sake of eliminating background interference; the second was to analyze any change of the fastener position for looseness detection. This algorithm was executed and verified through deep convolutional networks. Firstly

the areas of these two kinds of fasteners were located through YOLO V2 network; secondly

segmentation was conducted at the edge of the connecting plate

the insulator

the bolt and the screw

and the head of the connecting plate screw through the Mask R-CNN network simultaneously; finally

looseness of fasteners was detected by judging whether the position of any segmented part was changed. The proposed defect detection algorithm was tested using the data of the medium and low speed maglev system in Changsha

which revealed a looseness detection accuracy of over 90% for both the base mounting bolts and the connecting plate screws

and a recall rate of over 94%. The test results show that the method presented in this paper can accurately identify the loosened fasteners on the conductor rails of the medium and low speed maglev railways.

关键词

中低速磁浮接触轨紧固件松动YOLO V2网络Mask R-CNN网络Radon变换

Keywords

conductor rail of medium and low speed maglev railwaysfastener loosenessYOLO V2 networkMask R-CNN networkRadon transform

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