Crossing‐Gate Rod Breakage Detection in a Railway Telemeter System Using Machine Learning.

Autor:	Kashiwao, Tomoaki, Tanoue, Hiroya, Shiraishi, Noriyuki, Misaki, Yuki, Ando, Takashi, Ikeda, Kenji, Tanaka, Daisuke
Předmět:	SUPPORT vector machines RANDOM forest algorithms RAILROAD companies ELECTRICAL engineers MACHINE learning
Zdroj:	IEEJ Transactions on Electrical & Electronic Engineering; Jan2023, Vol. 18 Issue 1, p156-158, 3p
Abstrakt:	Shikoku Railway Company (JR Shikoku) has installed a telemeter system whose network spreads across all the railway lines in the Shikoku area. The telemeter system monitors and collects real‐time data, e.g. current, voltage, and relay signals of railway equipment. We propose a method of detecting the breakage of a crossing‐gate rod based on big data using representative machine‐learning techniques, namely, random forest and support vector machine. Moreover, we evaluate the proposed method for rod breakage cases at seven locations and demonstrate its effectiveness and versatility. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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