Author:
Tsunashima Hitoshi,Takikawa Masashi
Abstract
Condition monitoring of railway tracks is effective for the sake of an increase in the safety of regional railways. This study proposes a new method for automatically classifying the type and degradation level of track fault using a convolutional neural network (CNN), which is a machine learning method, by imaging car body acceleration on a time-frequency plane by continuous wavelet transform. As a result of applying this method to the data measured in regional railways, it was possible to classify and extract the sections that need repair according to the degree of deterioration of the tracks, and to identify the track fault in those sections.
Reference19 articles.
1. Weston P, Roberts C, Yeo G, Stewar E. Perspectives on railway track geometry condition monitoring from in-service railway vehicles. Vehicle System Dynamics. 2015;53(7):1063-1091. DOI: 10.1080/00423114.2015.1034730
2. Tsunashima H, Mori H, Ogino M, Asano A. Development of track condition monitoring system using onboard sensing device. In: Zboinski K. editors. Railway Research. London: IntechOpen; 2015. p. 145. DOI: 10.5772/61077
3. Mori H, Ohno H, Tsunashima H, Saito Y. Development of compact size onboard device for condition monitoring of railway tracks. Journal of Mechanical Systems for Transportation and Logistics. 2013;6(2):142-149. DOI: 10.1299/jmtl.6.142
4. Vinberg EM, Martin M, Firdaus AH, Tang Y, Qazizadeh A. Railway Applications of Condition Monitoring. Stockholm, Sweden: KTH Royal Institute of Technology; 2018. p. 147. DOI: 10.13140/RG.2.2.35912.62729
5. Tsunashima H. Condition monitoring of railway tracks from car-body vibration using a machine learning technique. Applied Sciences. 2019;9(13):2734. DOI: 10.3390/app9132734
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