Fault Inspection of Brake Shoe Wear for TEDS Based on Machine...

来自 : 发布时间：2024-05-14

Part of the Communications in Computer and Information Science book series (CCIS, volume 1415)AbstractWith rapid development of high-speed railway, the automated inspection of related faults is prerequisite for ensuring the safety of the EMU movement. As similar as TFDS in moving freight trains, TEDS is also applying in fault detection of the running EMU. Brake shoe is one of the key components in brake system, and the pre-warning on wear limitation about it is very important in the fault detection on EMU. In this paper, we propose a real-time, automated inspection method to detect the wear limitation of the brake shoe. Firstly, multi-resolution pyramid template matching technology is used for fast localization of the brake shoe. Then, LSD and Hough transform are employed to detect the wear limitation of the brake shoe. The experiments demonstrate that the way is high efficiency and well real-time, and it can meet the need of practical application.KeywordsFault inspection Machine vision TEDS LSD Hough transform This is a preview of subscription content, log in to check access.NotesAcknowledgmentsThis work was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20191012) and the Scientific Research Foundation of Nanjing Institute of Technology (Grant No. JCYJ201822).References1.Liu, R., Wang, Y.: Principle and Application of TFDS. China Railway Publication, Beijing (2005). (in chinese)Google Scholar2.Zhou, F., Rong, Z., et al.: Dust collector localization in trouble of moving freight car detection system. J. Zhejiang Univ. C 14(2), 98–106 (2013).  https://doi.org/10.1631/jzus.C1200223CrossRefGoogle Scholar3.Gioi, R.G.V., Jakubowicz, J., Morel, J.M., et al.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2010).  https://doi.org/10.1109/TPAMI.2008.300CrossRefGoogle Scholar4.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Upper Saddle (2007)Google ScholarCopyright information© Springer Nature Singapore Pte Ltd. 2021Authors and AffiliationsMengqiong Ge1Shengfang Lu1Yan Zhang2Cui Qin1Email author1.School of Information and Communication EngineeringNanjing Institute of TechnologyNanjingChina2.Swissgrid Ltd, Short-Term Network ModellingAarauSwitzerlandAbout this paper CrossMark Ge M., Lu S., Zhang Y., Qin C. (2021) Fault Inspection of Brake Shoe Wear for TEDS Based on Machine Vision. In: Tian Y., Ma T., Khan M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_5