Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
University of Žilina, Slovakia
Abstract
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.
Description
M. Sysyn: ORCID 0000-0001-6893-0018, O. Nabochenko: ORCID 0000-0001-6048-2556, V. Kovalchuk: ORCID 0000-0003-4350-1756
Keywords
turnouts, inertial measurement systems, predictive maintenance, signal processing, data mining, machine learning, data reduction, feature selection, КРС (ЛФ)
Citation
Sysyn M., Gruen D., Gerber U., Nabochenko O., Kovalchuk V. Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach. Communications – Scientific Letters of the University of Zilina. 2019. Vol. 21, iss. 1. P. 42–48. DOI: 10.26552/com.C.2019.1.42-48.