Communications - Scientific Letters of the University of Zilina 2019, 21(1):42-48 | DOI: 10.26552/com.C.2019.1.42-48

Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach

Mykola Sysyn1, Dimitri Gruen1, Ulf Gerber2, Olga Nabochenko3, Vitalii Kovalchuk3
1 Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany
2 Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany
3 Department of the Rolling Stock and Track, Lviv branch of Dnipropetrovsk National University of Railway Transport, Lviv, Ukraine

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.

Keywords: turnouts; inertial measurement systems; predictive maintenance; signal processing; data mining; machine learning; data reduction; feature selection

Received: September 26, 2018; Accepted: November 16, 2018; Published: February 20, 2019  Show citation

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Sysyn, M., Gruen, D., Gerber, U., Nabochenko, O., & Kovalchuk, V. (2019). Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach. Communications - Scientific Letters of the University of Zilina, 21(1), 42-48. doi: 10.26552/com.C.2019.1.42-48
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