Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection
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 r...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/4/493 |
| _version_ | 1797604617217900544 |
|---|---|
| author | Marta Zamorano María Jesús Gómez Cristina Castejón |
| author_facet | Marta Zamorano María Jesús Gómez Cristina Castejón |
| author_sort | Marta Zamorano |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:49:17Z |
| format | Article |
| id | doaj.art-9dea81831c0147fea5c94f6a8b7b28cb |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-11T04:49:17Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-9dea81831c0147fea5c94f6a8b7b28cb2023-11-17T20:09:23ZengMDPI AGMachines2075-17022023-04-0111449310.3390/machines11040493Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles DetectionMarta Zamorano0María Jesús Gómez1Cristina Castejón2MAQLAB Group, Mechanical Engineering Department, Universidad Carlos III de Madrid, Av. de la Universidad 30, 28911 Leganés, SpainMAQLAB Group, Mechanical Engineering Department, Universidad Carlos III de Madrid, Av. de la Universidad 30, 28911 Leganés, SpainMAQLAB Group, Mechanical Engineering Department, Universidad Carlos III de Madrid, Av. de la Universidad 30, 28911 Leganés, SpainThe 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.https://www.mdpi.com/2075-1702/11/4/493mother waveletwavelet packet transformvibration analysiscondition monitoringrailway axlescrack detection |
| spellingShingle | Marta Zamorano María Jesús Gómez Cristina Castejón Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection Machines mother wavelet wavelet packet transform vibration analysis condition monitoring railway axles crack detection |
| title | Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection |
| title_full | Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection |
| title_fullStr | Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection |
| title_full_unstemmed | Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection |
| title_short | Optimal Selection of the Mother Wavelet in WPT Analysis and Its Influence in Cracked Railway Axles Detection |
| title_sort | optimal selection of the mother wavelet in wpt analysis and its influence in cracked railway axles detection |
| topic | mother wavelet wavelet packet transform vibration analysis condition monitoring railway axles crack detection |
| url | https://www.mdpi.com/2075-1702/11/4/493 |
| work_keys_str_mv | AT martazamorano optimalselectionofthemotherwaveletinwptanalysisanditsinfluenceincrackedrailwayaxlesdetection AT mariajesusgomez optimalselectionofthemotherwaveletinwptanalysisanditsinfluenceincrackedrailwayaxlesdetection AT cristinacastejon optimalselectionofthemotherwaveletinwptanalysisanditsinfluenceincrackedrailwayaxlesdetection |