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Title: Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach
Authors: Sysyn, Mykola
Gruen, Dimitri
Gerber, Ulf
Nabochenko, Olga
Kovalchuk, Vitalii
Keywords: turnouts
inertial measurement systems
predictive maintenance
signal processing
data mining
machine learning
data reduction
feature selection
Issue Date: 2019
Publisher: University of Žilina, Slovakia
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.
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
ISSN: 1335-4205 (print)
2585-7878 (online)
Other Identifiers: DOI: 10.26552/com.C.2019.1.42-48
Appears in Collections:Статті КРС (ЛФ)

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