Open AccessINDICATORS FOR COMMON CROSSING STRUCTURAL HEALTH MONITORING WITH TRACK-SIDE INERTIAL MEASUREMENTS
Author(s) -
Mykola Sysyn,
Ulf Gerber,
Olga Nabochenko,
Yangyang Li,
Vitalii Kovalchuk
Publication year - 2019
Publication title -
acta polytechnica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.207
H-Index - 15
eISSN - 1805-2363
pISSN - 1210-2709
DOI - 10.14311/ap.2019.59.0170
Subject(s) - acceleration , ranking (information retrieval) , computer science , accelerometer , track (disk drive) , principal component analysis , data mining , wavelet , feature (linguistics) , reliability engineering , artificial intelligence , engineering , linguistics , philosophy , physics , classical mechanics , operating system
This paper focuses on the experimental study of an alteration in the railway crossing dynamic response due to the rolling surface degradation during a crossing’s lifecycle. The maximal acceleration measured with the track-side measurement system as well as the impact position monitoring show no significant statistical relation to the rolling surface degradation. The additional spectral features are extracted from the acceleration measurements with a wavelet transform to improve the information usage. The reliable prediction of the railway crossing remaining useful life (RUL) demands the trustworthy indicators of structural health that systematically change during the lifecycle. The popular simple machine learning methods like principal component analysis and partial least square regression are used to retrieve two indicators from the experimental information. The feature ranking and selection are used to remove the redundant information and increase the relation of indicators to the lifetime.