RT Journal Article
SR Electronic
A1 Sysyn, Mykola
A1 Gruen, Dimitri
A1 Gerber, Ulf
A1 Nabochenko, Olga
A1 Kovalchuk, Vitalii
T1 Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach
JF Communications - Scientific Letters of the University of Zilina
YR 2019
VO 21
IS 1
SP 42
OP 48
DO 10.26552/com.C.2019.1.42-48
UL https://komunikacie.uniza.sk/artkey/csl-201901-0007.php
AB 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.