Full Text:   <3002>

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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: 7583

Citations:  Bibtex RefMan EndNote GB/T7714


Rong Zou


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


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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T1 - Real-time monitoring of brake shoe keys in freight cars
A1 - Rong Zou
A1 - Zhen-ying Xu
A1 - Jin-yang Li
A1 - Fu-qiang Zhou
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1400305

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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