RT Journal Article
T1 Optimal selection of the mother wavelet in WPT analysis and its influence in cracked railway axles detection
A1 Zamorano Garzón, Marta
A1 Gómez García, María Jesús
A1 Castejón Sisamón, Cristina
AB The detection of cracked railway axles by processing vibratory signals measured during operation is the focus of this study. The rotodynamic theory is applied to this specific purpose but, in practice and for real systems, there is no consensus on applying the results obtained from theory. Finding reliable patterns that change during operation would have advantages over other currently applied methods, such as non-destructive testing (NDT) techniques, because data between inspections would be obtained during operation. Vibratory signal processing techniques in the time-frequency domain, such as wavelet packet transform (WPT), have proved to be reliable to obtain patterns. The aim of this work is to develop a methodology to select the optimal function associated with the WPT, the mother wavelet (MW), and to find diagnostic patterns for cracked railway axle detection. In previous related works, the Daubechies 6 MW was commonly used for all speed/load conditions and defects. In this work, it was found that the Symlet 9 MW works better, so a comparative study was carried out with both functions, and it was observed that the success rates obtained with Daubechies 6 are improved when using Symlet 9. Specifically, defects above 16.6% of the shaft diameter were reliably detected, with no false alarms. To validate the proposed methodology, experimental vibratory signals of a healthy scaled railway axle were obtained and then the same axle was tested with a transverse crack located close to a section change (where this type of defect typically appears) for nine different crack depths.
PB MDPI
SN 2075-1702
YR 2023
FD 2023-04
LK https://hdl.handle.net/10016/37244
UL https://hdl.handle.net/10016/37244
LA eng
NO This article belongs to the Special Issue Railway Vehicle Maintenance 4.0.
NO This research was funded by the MCIN/AEI/10.13039/501100011033, grant number MC4.0 PID2020-116984RB-C22.
DS e-Archivo
RD 18 jul. 2024