Real-time monitoring of brake shoe keys in freight cars

Xu, Zhenying; Zou, Rong; Li, Jinyang; Zhou, Fuqiang

doi:10.1631/fitee.1400305

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…Faults of running trains can be generally divided into two categories: visible faults and hidden faults. Visible faults mainly include missing [1], [2], displacement [3] and deformation of larger parts. Hidden faults consist of various gradual faults, such as loose wire breakage, anti-loose mark ectopic and bolt-loosening [4].…”

Section: Introductionmentioning

confidence: 99%

A Fast Bolt-Loosening Detection Method of Running Train’s Key Components Based on Binocular Vision

2019

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Bolt-loosening can cause poor running quality of trains, even resulting in terrible accidents. Currently, existing bolt-loosening detection methods for running trains need 3D data of the whole train body, extremely decreasing the efficiency of fault detection. In this paper, we propose a fast bolt-loosening detection method for the running train's key components based on binocular vision. Since a train generally consists of many cars with the same structure, the position distribution of train's key components is regular and periodic. First, we propose a novel method to detect key component regions including bolts, taking full advantage of this periodic distribution rule. Second, the sub-pixel edges of the bolt cap and mounting surface in the localized regions are extracted and segmented, respectively, combining with the convolutional neural network (CNN). Finally, based on stereo matching and the binocular vision model, the 3D data of these edges are obtained to calculate the distance between the bolt cap and mounting surface. By comparing the calculated distance with the reference value, we can judge whether bolt-loosening has occurred. The experimental results indicate that multi-bolt looseness can be calculated simultaneously. The measurement repeatability and precision are superior to 0.03 and 0.08 mm, respectively, and the relative error is less than 1.42%. INDEX TERMS Binocular stereo vision, bolt loosening, convolutional neural network, edge extraction.

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Section: Introductionmentioning

confidence: 99%

A Fast Bolt-Loosening Detection Method of Running Train’s Key Components Based on Binocular Vision

2019

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“…Zou et al. 6 detected brake shoe key condition using vision approach and obtained a high inspection rate. Zhou et al.…”

Section: Introductionmentioning

confidence: 99%

Automated visual inspection of multipattern train components using gradient information and feature fusion under the illumination-variant condition

Guo

Gao

2017

Proceedings of the Institution of Mechanical Engineers, Part F:

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The condition of train components needs to be inspected periodically to ensure the quality and safe operation of trains. The brake beam bolt is an important locking part in the main structure of the support and connection components. Any fault in the break beam bolt can lead to serious accidents. With the development of computer vision techniques, automated visual inspection methods have started replacing routine manual checking. This paper presents an automated and hierarchical inspection method for the detection of the condition of the brake beam bolt. Gradient information and basic feature descriptors are fused into a synthesized feature expression for eliminating the influence of the illumination-variant condition. The fused feature is also employed in the identification of the object condition. The results of the experiment show that the feature fusion expression form can effectively solve the problem caused by the illumination-variant scene and condition. The correct rates of localization of joint part and target fault detection have shown high accuracy. It has been concluded that the overall performance of the visual inspection system can meet the practical object safety assurance application.

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“…Therefore, the ministry of railways is vigorously to promote a safety inspection system──Trouble of moving freight car detection system (TFDS) [1,2]. TFDS uses equipment to shoot fast-moving freight train, image acquisition, and then the use of computer image recognition, thus picking fault [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Detecting Side Frame Key Using Histogram of Oriented Gradient and Local Binary Pattern

Zhang¹,

Wang²,

Feng³

et al. 2017

IJCA

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Real-time monitoring of brake shoe keys in freight cars

Cited by 12 publications

References 29 publications

A Fast Bolt-Loosening Detection Method of Running Train’s Key Components Based on Binocular Vision

A Fast Bolt-Loosening Detection Method of Running Train’s Key Components Based on Binocular Vision

Automated visual inspection of multipattern train components using gradient information and feature fusion under the illumination-variant condition

An Automatic Method for Detecting Side Frame Key Using Histogram of Oriented Gradient and Local Binary Pattern

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