Full Text:   <1345>

Summary:  <617>

CLC number: TP277; U279.3

On-line Access: 2015-03-04

Received: 2014-08-26

Revision Accepted: 2014-11-15

Crosschecked: 2015-01-28

Cited: 2

Clicked: 2682

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Rong Zou

http://orcid.org/0000-0002-2297-1348

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.3 P.191-204

http://doi.org/10.1631/FITEE.1400305


Real-time monitoring of brake shoe keys in freight cars


Author(s):  Rong Zou, Zhen-ying Xu, Jin-yang Li, Fu-qiang Zhou

Affiliation(s):  School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China; more

Corresponding email(s):   zr@ujs.edu.cn

Key Words:  Condition monitoring, Feature expression, Brake shoe key, Machine vision


Rong Zou, Zhen-ying Xu, Jin-yang Li, Fu-qiang Zhou. Real-time monitoring of brake shoe keys in freight cars[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(3): 191-204.

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Abstract: 
condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key (BSK) is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent inspection process is divided into three stages: first, the region-of-interest (ROI) is segmented from the source image by an improved spatial pyramid matching scheme based on multi-scale census transform (MSCT). To localize the BSK in the ROI, census transform (CT) on gradient images is developed in the second stage. Then gradient encoding histogram (GEH) features and linear support vector machines (SVMs) are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.

The topic is interesting, as machine vision technology offers a quick and automatic manner to monitor the condition of railroad equipment and components. The paper reports a method for automated visual inspection of brake shoe key (BSK) condition in freight cars.

铁路货车闸瓦钎故障的实时监控

目的:面向铁路货车关键机械部件的健康状态监控,针对铁路货车闸瓦钎这种复杂机械部件,实现基于视觉图像的户外全天候实时自动故障检测。
创新点:针对闸瓦钎这种复杂目标机械部件的故障检测,提出一种新颖的实时精确故障检测方法。鉴于目标部件故障样本和无故障样本存在极强的类间相似性和类内差异性,情况相对复杂,提出采用多特征多层级方式。多特征避免单一特征的局限性和片面性,满足系统高精度要求,而多层级级联方式可事先排除大量无关背景信息,满足系统实时性需求。
方法:采用层次化故障检测思路,在ROI分割上(图10),提出采用多尺度中心变换编码(MSCT),通过构建改进的空间金字塔方式实现。在闸瓦钎定位上,在梯度域对闸瓦钎部位进行中心变换编码,以梯度编码直方图(HEG)特征构建特征向量,采用SVM训练生成定位分类器。故障状态分类器的构建与之相似,但编码是建立在灰度图像基础上,最终在分割出的ROI中通过定位和判别分类器级联方式实现闸瓦钎丢失故障的全自动检测,无需任何人工参与过程。
结论:针对现有铁路故障检测技术存在的不足,提供一种铁路货车闸瓦钎丢失故障的自动检测方法,既可降低铁路货车故障检测成本,又可提高铁路货车故障检测效率,为铁路提速提供了可靠的安全保障。相应实验表明该系统故障检测率达到了99.2%(表2),而检测速度接近5帧/秒,具有很好的实时性和很高的检测精度。

关键词:状态监控;特征提取;闸瓦钎故障;机器视觉

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