Browsing by Author "Gruen, Dimitri"

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    Improvement of Inspection System for Common Crossings by Track Side Monitoring and Prognostics
    (Techno-Press, South Korea, 2020) Sysyn, Mykola; Nabochenko, Olga; Kovalchuk, Vitalii V.; Gruen, Dimitri; Pentsak, Andriy
    EN: 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.
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    Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach
    (University of Žilina, Slovakia, 2019) Sysyn, Mykola; Gruen, Dimitri; Gerber, Ulf; Nabochenko, Olga; Kovalchuk, Vitalii V.
    EN: A machine learning approach for the recent detection of crossing faults is presented in the paper. The basis for the research are the data of the axle box inertial measurements on operational trains with the system ESAH-F. Within the machine learning approach the signal processing methods, as well as data reduction classification methods, are used. The wavelet analysis is applied to detect the spectral features at measured signals. The simple filter approach and sequential feature selection is used to find the most significant features and train the classification model. The validation and error estimates are presented and its relation to the number of selected features is analysed, as well.