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Development of Railway Vibration Evaluation System Using Actual Railway Vibration Database

실측 철도 진동 데이터베이스를 이용한 철도진동 평가 시스템 개발

  • 이현준 (알엠에스테크놀러지(주) 연구소) ;
  • 서은성 (알엠에스테크놀러지(주) 연구소) ;
  • 황영섭 (선문대학교 컴퓨터공학부)
  • Received : 2018.11.06
  • Accepted : 2019.01.07
  • Published : 2019.04.30

Abstract

Recently, it is necessary to develop a technology for quantitatively evaluating railway vibration to prevent civil complaints about orbital structures caused by railway noise and normal operation of ultra-precise equipment of orbital industrial complexes. The existing analytical method requires a very complicated dynamic response model, and it is difficult to secure the reliability of the result due to the inaccuracy of the demand model. Therefore, in this paper, we propose a railway vibration evaluation algorithm and system that deduce the vibration value generated from railway operation by using Linear Regression and Gradient Descent technique based on actual measurement railway vibration database that classifies factors affecting railway vibration. The prediction results obtained by the proposed algorithm show higher efficiency and accuracy than the existing analytical methods.

최근 철도소음으로 인해 발생하는 궤도 주변 구조물의 민원 방지와 궤도 주변 산업단지의 초정밀 장비들의 정상적인 운영을 위해 철도 진동을 정량적으로 평가할 수 있는 기술개발이 필요하다. 기존의 해석적인 방법은 매우 복잡한 동적 응답 모델이 요구되며, 요구 모델의 부정확성으로 인한 결과의 신뢰성을 확보하기 어려운 문제가 있다. 따라서, 본 논문에서는 철도 진동에 영향을 주는 요소들을 분류한 국내 철도진동 실측 데이터베이스를 기반으로 Linear Regression, Gradient Descent 기법을 이용해 철도 운행으로부터 발생되는 진동값을 추론하는 철도진동 평가 알고리즘 및 시스템을 제안한다. 제안된 알고리즘으로 얻은 추론결과는 기존의 해석적 방법에 비해 높은 효율성과 정확성을 보인다.

Keywords

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Fig. 1. Effect of Factors Affecting Railway Vibration and Noise

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Fig. 2. Vibration Data Impact of Rolling Stock

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Fig. 3. Trend of Railway Vibration on Distance

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Fig. 5. New Data Input

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Fig. 6. Prediction Algorithm Process

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Fig. 7. Web-based Railway Vibration&Noise Prediction System

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Fig. 8. Number of Vibration-Induced Factors Selected

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Fig. 9. Result of Prediction on System

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Fig. 10. Place of Measurement & Vibration Sensor

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Fig. 11. Result of Measurement

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Fig. 12. Analytical Model

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Fig. 13. Vibration Input Source[4]

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Fig. 14. Vibration Response Distribution

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Fig. 15. Comparison of Measured Values, Analytical Values, and Inference Values

Table 1. Railway Vibration Impact Factor

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Table 2. Activation Function

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Fig. 4. Example of Hypothesis

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Table 3. Result of Hypothesis Cost

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Table 4. Example of Input Data

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Table 5. Result of Prediction

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Table 6. Input Data

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Table 7. Accuracy Comparison (Analysis or Prediction / Real Data * 100)

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