Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach
The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3609 |
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| author | Mykola Sysyn Michal Przybylowicz Olga Nabochenko Lei Kou |
| author_facet | Mykola Sysyn Michal Przybylowicz Olga Nabochenko Lei Kou |
| author_sort | Mykola Sysyn |
| collection | DOAJ |
| description | The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement. |
| first_indexed | 2024-03-10T11:09:22Z |
| format | Article |
| id | doaj.art-73b9fc40048e49bebd454cb20fa2aba9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:09:22Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-73b9fc40048e49bebd454cb20fa2aba92023-11-21T20:53:58ZengMDPI AGSensors1424-82202021-05-012111360910.3390/s21113609Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven ApproachMykola Sysyn0Michal Przybylowicz1Olga Nabochenko2Lei Kou3Institute of Railway Systems and Public Transport, Technical University of Dresden, 01069 Dresden, GermanyInstitute of Railway Systems and Public Transport, Technical University of Dresden, 01069 Dresden, GermanyDepartment of the Rolling Stock and Track, Lviv Branch of Dnipro National University of Railway Transport Named after Academician V. Lazaryan, 79052 Lviv, UkraineInstitute of Railway Systems and Public Transport, Technical University of Dresden, 01069 Dresden, GermanyThe ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.https://www.mdpi.com/1424-8220/21/11/3609ballasted track superstructuresleeper support conditiondynamic simulationtrack-side and on-board measurementrail deflectionwavelet scattering |
| spellingShingle | Mykola Sysyn Michal Przybylowicz Olga Nabochenko Lei Kou Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach Sensors ballasted track superstructure sleeper support condition dynamic simulation track-side and on-board measurement rail deflection wavelet scattering |
| title | Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach |
| title_full | Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach |
| title_fullStr | Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach |
| title_full_unstemmed | Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach |
| title_short | Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach |
| title_sort | identification of sleeper support conditions using mechanical model supported data driven approach |
| topic | ballasted track superstructure sleeper support condition dynamic simulation track-side and on-board measurement rail deflection wavelet scattering |
| url | https://www.mdpi.com/1424-8220/21/11/3609 |
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