Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines
Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/12/3575 |
| _version_ | 1827714358310862848 |
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| author | María Jesús Gómez Cristina Castejón Eduardo Corral Juan Carlos García-Prada |
| author_facet | María Jesús Gómez Cristina Castejón Eduardo Corral Juan Carlos García-Prada |
| author_sort | María Jesús Gómez |
| collection | DOAJ |
| description | Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed. |
| first_indexed | 2024-03-10T18:54:35Z |
| format | Article |
| id | doaj.art-b784419a32094b63ab401ca6974de790 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:54:35Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b784419a32094b63ab401ca6974de7902023-11-20T04:51:20ZengMDPI AGSensors1424-82202020-06-012012357510.3390/s20123575Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector MachinesMaría Jesús Gómez0Cristina Castejón1Eduardo Corral2Juan Carlos García-Prada3Mechanical Department, Universidad Carlos III de Madrid (UC3M), 28982 Leganés, SpainMechanical Department, Universidad Carlos III de Madrid (UC3M), 28982 Leganés, SpainMechanical Department, Universidad Carlos III de Madrid (UC3M), 28982 Leganés, SpainMechanical Department, Universidad Nacional de Education a Distancia (UNED), 28040 Madrid, SpainRailway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.https://www.mdpi.com/1424-8220/20/12/3575railway axleswavelet packet transformbogie testingcondition monitoringsupport vector machines |
| spellingShingle | María Jesús Gómez Cristina Castejón Eduardo Corral Juan Carlos García-Prada Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines Sensors railway axles wavelet packet transform bogie testing condition monitoring support vector machines |
| title | Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines |
| title_full | Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines |
| title_fullStr | Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines |
| title_full_unstemmed | Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines |
| title_short | Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines |
| title_sort | railway axle condition monitoring technique based on wavelet packet transform features and support vector machines |
| topic | railway axles wavelet packet transform bogie testing condition monitoring support vector machines |
| url | https://www.mdpi.com/1424-8220/20/12/3575 |
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