Presentation 2018-12-14
Prosal of Practical Method of track condition monitoring compact device using machine learning
Shohei Wakai, Tetsuya Takata, Takeshi Mizuma, Hitoshi Tsunashima, Akira Matsumoto, Yuichi Hayashida, Ryota Hirose,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In local railway, there are few railway operators who chan conduct sufficient inspection of track condition due to cost etc. In response to this problem, track condition monitoring compact device by simple method was developed to local railway. There is a possibility to utilize measurement data by using machine learning. In this research, we propose practical method of track condition monitoring compact device using machine learning.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Railway / Condition monitoring / Machine learning / Big data analysis / Track / Maintenance
Paper # DC2018-62
Date of Issue 2018-12-07 (DC)

Conference Information
Committee DC
Conference Date 2018/12/14(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Miyako Seisyonen-No-Ie
Topics (in Japanese) (See Japanese page)
Topics (in English) 3rd Winter Workshop on safety
Chair Satoshi Fukumoto(Tokyo Metropolitan Univ.)
Vice Chair Hiroshi Takahashi(Ehime Univ.)
Secretary Hiroshi Takahashi(Tokyo Inst. of Tech.)
Assistant

Paper Information
Registration To Technical Committee on Dependable Computing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Prosal of Practical Method of track condition monitoring compact device using machine learning
Sub Title (in English)
Keyword(1) Railway
Keyword(2) Condition monitoring
Keyword(3) Machine learning
Keyword(4) Big data analysis
Keyword(5) Track
Keyword(6) Maintenance
1st Author's Name Shohei Wakai
1st Author's Affiliation Kyosan Electric Manufacturing Co.,Ltd.(Kyosan Electric Manufacturing)
2nd Author's Name Tetsuya Takata
2nd Author's Affiliation Kyosan Electric Manufacturing Co.,Ltd.(Kyosan Electric Manufacturing)
3rd Author's Name Takeshi Mizuma
3rd Author's Affiliation Tokyo University(Tokyo Univ.)
4th Author's Name Hitoshi Tsunashima
4th Author's Affiliation Nihon University(Nihon Univ.)
5th Author's Name Akira Matsumoto
5th Author's Affiliation Nihon University(Nihon Univ.)
6th Author's Name Yuichi Hayashida
6th Author's Affiliation Nihon University(Nihon Univ.)
7th Author's Name Ryota Hirose
7th Author's Affiliation Nihon University(Nihon Univ.)
Date 2018-12-14
Paper # DC2018-62
Volume (vol) vol.118
Number (no) DC-364
Page pp.pp.25-28(DC),
#Pages 4
Date of Issue 2018-12-07 (DC)