Structural Monitoring and Maintenance

  • 제6권3호
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  • Pages.219-235
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  • 2019
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  • 2288-6605(pISSN)
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  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Improvement of inspection system for common crossings by track side monitoring and prognostics

  • Sysyn, Mykola (Institute of Railway Systems and Public Transport, Technical University of Dresden) ;
  • Nabochenko, Olga (Department of the rolling stock and track, Lviv branch of Dnipro National University of Railway Transport) ;
  • Kovalchuk, Vitalii (Department of the rolling stock and track, Lviv branch of Dnipro National University of Railway Transport) ;
  • Gruen, Dimitri (Institute of Railway Systems and Public Transport, Technical University of Dresden) ;
  • Pentsak, Andriy (Department of Construction industry, Lviv Polytechnic National University)
  • 투고 : 2019.05.13
  • 심사 : 2019.07.17
  • 발행 : 2019.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

키워드

참고문헌

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피인용 문헌

  1. Improvement recommendations for railway infrastructure maintenance vol.157, 2019, https://doi.org/10.1051/e3sconf/202015701001
  2. Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach vol.21, pp.11, 2019, https://doi.org/10.3390/s21113609