Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach
Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach
Author(s): Mykola Sysyn, Dimitri Gruen, Ulf Gerber, Olga Nabochenko, Vitalii KovalchukSubject(s): Transport / Logistics
Published by: Žilinská univerzita v Žilině
Keywords: turnouts; inertial measurement systems; predictive maintenance; signal processing; data mining; machine learning; data reduction; feature selection;
Summary/Abstract: 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.
Journal: Komunikácie - vedecké listy Žilinskej univerzity v Žiline
- Issue Year: 21/2019
- Issue No: 1
- Page Range: 42-48
- Page Count: 7
- Language: English
