한국안전학회지 (Journal of the Korean Society of Safety)

  • 제38권4호
  • /
  • Pages.1-7
  • /
  • 2023
  • /
  • 1738-3803(pISSN)
  • /
  • 2383-9953(eISSN)
한국안전학회 (The Korean Society of Safety)
권호 표지
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도시철도차량 주행차륜의 직경/플랜지 변화 데이터와 머신러닝 기법을 활용한 주행거리 예측 연구

A Study on the Mileage Prediction of Urban Railway Vehicle using Wheel Diameter/Flange change Data and Machine Learning Techniques

  • 노학락 (서울과학기술대학교 글로벌철도시스템공학과) ;
  • 임원식 (서울과학기술대학교 기계자동차공학과)
  • Hak Rak Noh (Department of Global Railway Systems, Seoul National University of Science and Technology) ;
  • Won Sik Lim (Dep. Mechanical and Automotive Engineering, Seoul National University of Science and Technology)
  • 투고 : 2023.07.20
  • 심사 : 2023.08.16
  • 발행 : 2023.08.31
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초록

The steel wheels of urban railway vehicles gather a lot of data through regular measurements during maintenance. However, limited research has been carried out utilizing this data, resulting in difficulties predicting the maintenance period. This paper studied a machine learning model suitable for mileage prediction by studying the characteristics of mileage change according to diameter and flange thickness changes. The results of this study indicate that the larger the diameter, the longer the travel distance, and the longest flange thickness is at 30 mm, which gradually shortened at other times. As a result of research on the machine learning prediction model, it was confirmed that the random forest model is the optimal model with a high coefficient of determination and a low root mean square error.

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참고문헌

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