Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning
Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/9/5376 |
| _version_ | 1797603088723345408 |
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| author | Yang Zuo Jan Lundberg Praneeth Chandran Matti Rantatalo |
| author_facet | Yang Zuo Jan Lundberg Praneeth Chandran Matti Rantatalo |
| author_sort | Yang Zuo |
| collection | DOAJ |
| description | Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges. |
| first_indexed | 2024-03-11T04:24:30Z |
| format | Article |
| id | doaj.art-b85a623a3e494111b6e51d083462ad88 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T04:24:30Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b85a623a3e494111b6e51d083462ad882023-11-17T22:33:09ZengMDPI AGApplied Sciences2076-34172023-04-01139537610.3390/app13095376Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine LearningYang Zuo0Jan Lundberg1Praneeth Chandran2Matti Rantatalo3Operation and Maintenance Group, Luleå University of Technology, 97187 Luleå, SwedenOperation and Maintenance Group, Luleå University of Technology, 97187 Luleå, SwedenOperation and Maintenance Group, Luleå University of Technology, 97187 Luleå, SwedenOperation and Maintenance Group, Luleå University of Technology, 97187 Luleå, SwedenSwitches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges.https://www.mdpi.com/2076-3417/13/9/5376railwayS&Cswitch and crossingsensoraccelerometersvibration |
| spellingShingle | Yang Zuo Jan Lundberg Praneeth Chandran Matti Rantatalo Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning Applied Sciences railway S&C switch and crossing sensor accelerometers vibration |
| title | Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning |
| title_full | Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning |
| title_fullStr | Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning |
| title_full_unstemmed | Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning |
| title_short | Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning |
| title_sort | squat detection and estimation for railway switches and crossings utilising unsupervised machine learning |
| topic | railway S&C switch and crossing sensor accelerometers vibration |
| url | https://www.mdpi.com/2076-3417/13/9/5376 |
| work_keys_str_mv | AT yangzuo squatdetectionandestimationforrailwayswitchesandcrossingsutilisingunsupervisedmachinelearning AT janlundberg squatdetectionandestimationforrailwayswitchesandcrossingsutilisingunsupervisedmachinelearning AT praneethchandran squatdetectionandestimationforrailwayswitchesandcrossingsutilisingunsupervisedmachinelearning AT mattirantatalo squatdetectionandestimationforrailwayswitchesandcrossingsutilisingunsupervisedmachinelearning |