High-speed rail contact network U-holding nut fault detection algorithm

doi:10.3785/j.issn.1008-973X.2021.10.013

Journal of ZheJiang University (Engineering Science)

2021, Vol. 55

Issue (10): 1912-1921 DOI: 10.3785/j.issn.1008-973X.2021.10.013

High-speed rail contact network U-holding nut fault detection algorithm

Ying-jie NIU1(

),Yan-chen SU1,*(

),Dun-cheng CHENG1,Jia LIAO1,Hai-bo ZHAO2,Yong-qiang GAO3

1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2. CRRC Changchun Rail Bus Limited Company, Changchun 130000, China
3. Shenshuo Railway Branch, China Shenhua Energy Limited Company, Yulin 719000, China

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Abstract

The fault detection method combining the hoop nuts detector-net (HND-Net) and Mask_RCNN instance segmentation was proposed aiming at the problem of U-shaped hoop nut fault in the flat arm structure of high-speed railroad contact network. The proposed HND-Net target detection algorithm achieved the initial localization of the area where the U-shaped hoop of the flat wrist arm was located, and performed the pixel-level Mask_RCNN instance segmentation of the localized U-shaped hoop area in order to quickly obtain the precise localization and classification information of the four nuts of the U-shaped hoop of the flat wrist arm. The proposed segmentation algorithm achieved reliable fault diagnosis of U-shaped hoop nuts by using the obtained localization information. The experimental verification shows that the nut fault of U-type clamping hoop can be accurately located and detected in complex suspension images with good adaptability and high detection efficiency for shooting angle and shooting distance.

Key words： high-speed rail catenary U-shaped hoop HND-Net object detection instance segmentation fault detection

Received: 27 October 2020 Published: 27 October 2021

CLC:

U 225

Fund: 国家重点研发计划资助项目（2018YFB1201600）；神华科技创新资助项目（SHGF-18-57）

Corresponding Authors: Yan-chen SU E-mail: 445216101@qq.com;Su_yan_chen@126.com

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	Ying-jie NIU
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	Hai-bo ZHAO
	Yong-qiang GAO

Cite this article:

Ying-jie NIU,Yan-chen SU,Dun-cheng CHENG,Jia LIAO,Hai-bo ZHAO,Yong-qiang GAO. High-speed rail contact network U-holding nut fault detection algorithm. Journal of ZheJiang University (Engineering Science), 2021, 55(10): 1912-1921.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.10.013 OR https://www.zjujournals.com/eng/Y2021/V55/I10/1912

高铁接触网U型抱箍螺母故障检测算法

针对高速铁路接触网平腕臂结构中U型抱箍螺母故障的问题，提出结合抱箍螺母检测算法（HND-Net）与Mask_RCNN实例分割的故障检测方法. 提出HND-Net目标检测算法，实现对平腕臂U型抱箍所在区域的初定位，对于定位得到的U型抱箍区域图像进行像素级别的Mask_RCNN实例分割，快速得到平腕臂U型抱箍4颗螺母的精确定位及分类信息. 利用得到的定位信息，提出分割算法实现对U型抱箍螺母的可靠故障诊断. 经实验验证可知，利用该方法能够在复杂的悬挂图像中准确地定位检测U型抱箍的螺母故障，对于拍摄角度、拍摄距离有良好的适应性和较高的检测效率.

关键词： 高铁接触网, U型抱箍, HND-Net目标检测, 实例分割, 故障诊断

Fig.1 Structure of HND-Net model

Fig.2 Structure of residual module

Fig.3 Structure of residual feature augmentation module

Tab.1 Parameter table of enhanced feature

Fig.4 Structure of adaptive fusion module

Fig.5 Catenary suspension sample

Fig.6 Detection training loss curve

Fig.7 U-shaped hoop positioning image

Fig.8 U-shaped hoop capture image

Fig.9 Mask_RCNN network structure

Fig.10 Nut instance segmentation

Fig.11 Nut instance segmentation image

Fig.12 Nut1 missing

Fig.13 Nuts segmentation boxes image

Fig.14 Diagram of relationship between position of external rectangular frame of nut

Tab.2 Residual feature augmentation module ablation studies

Tab.3 Adaptive fusion module ablation studies

Tab.4 Comparison of detection precision, detection velocity and parameter number under different models

Fig.15 Proposed algorithm for double bracket detection map

Tab.5 U-shaped hoop experiment test data


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