RT Journal Article
T1 Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles
A1 Sánchez, René Vinicio
A1 Lucero, Pablo
A1 Macancela, Jean Carlo
A1 Rubio Alonso, Higinio
A1 Cerrada, Mariela
A1 Cabrera, Diego
A1 Castejón Sisamón, Cristina
AB Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under study. In the present work, the problem of the early crack detection in railway axles is addressed through condition-based monitoring, with the evaluation of several condition indicators of vibration signals on time and frequency domains. To achieve this goal, we applied two different approaches: in the first approach, we evaluate only the vibrations signals captured by accelerometers placed along the longitudinal direction and, in the second approach, a data fusion technique at the condition indicator level was conducted, evaluating six accelerometers by merging the indicator conditions according to the sensor placement. In both cases, a total of 54 condition indicators per vibration signal was calculated and selecting the best features by applying the Mean Decrease Accuracy method of Random Forest. Finally, we test the best indicators with a K-Nearest Neighbor classifier. For the data collection, a real bogie test bench has been used to simulate crack faults on the railway axles, and vibration signals from both the left and right sides of the axle were measured. The results not only show the performance of condition indicators in different domains, but also show that the fusion of condition indicators works well together to detect a crack fault in railway axles.
PB MDPI
SN 2076-3417
YR 2020
FD 2020-06-25
LK https://hdl.handle.net/10016/36039
UL https://hdl.handle.net/10016/36039
LA eng
NO Authors would like to thank the support provided by the Spanish Government, through the MAQ-STATUS DPI2015-69325-C2-1-R project, and Universidad Politécnica Salesiana through the research group GIDTEC.
DS e-Archivo
RD 17 jun. 2024