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Proceedings Paper

On-line bolt-loosening detection method of key components of running trains using binocular vision
Author(s): Yanxia Xie; Junhua Sun
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Paper Abstract

Bolt loosening, as one of hidden faults, affects the running quality of trains and even causes serious safety accidents. However, the developed fault detection approaches based on two-dimensional images cannot detect bolt-loosening due to lack of depth information. Therefore, we propose a novel online bolt-loosening detection method using binocular vision. Firstly, the target detection model based on convolutional neural network (CNN) is used to locate the target regions. And then, stereo matching and three-dimensional reconstruction are performed to detect bolt-loosening faults. The experimental results show that the looseness of multiple bolts can be characterized by the method simultaneously. The measurement repeatability and precision are less than 0.03mm, 0.09mm respectively, and its relative error is controlled within 1.09%.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060513 (15 November 2017); doi: 10.1117/12.2286728
Show Author Affiliations
Yanxia Xie, Beihang Univ. (China)
Junhua Sun, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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