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R. Krc¹, R. Ambur², Z. Hadas³, O. Olaby², I. Vukusic^1,4, O. Plasek¹, M. Entezami² and R. Dixon²

¹Faculty of Civil Engineering, Brno University of Technology, Czech Republic
²Birmingham Centre for Railway Research and Education, University of Birmingham, United Kingdom
³Faculty of Mechanical Engineering, Brno University of Technology, Czech Republic
⁴Vyzkumny Ustav Zeleznicni, a.s., Czech Republic

doi:10.4203/ccc.1.5.7

R. Krc, R. Ambur, Z. Hadas, O. Olaby, I. Vukusic, O. Plasek, M. Entezami, R. Dixon, "Condition Monitoring and Trend Analysis of Railway Turnouts Based on In-Situ Accelerometer Measurements", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 5.7, 2022, doi:10.4203/ccc.1.5.7

Keywords: railway, switches and crossings, accelerometer sensors, trend analysis, predictive maintenance.

Abstract

High dynamic forces at railway switches and crossings (S&C) are the primary cause of frequent defect formation. Regular acquisition of onsite sensory data aids condition evaluation or maintenance planning, which subsequently mitigates problems of unexpected malfunction of S&C components. Accelerometer data collected by in-situ sensors in UK and Czech Republic were used in this research for defining important metrics and validating prediction models. A number of metrics can be calculated from collected signals to provide information about the condition of S&C and its components. Change of these parameters over time is revealed by trend analysis and may signalize increased material deterioration or formation of a defect. Trend analysis methods span from simple regression to more advanced machine learning models for time series prediction and are listed in this paper. Evaluation of proposed models is performed on collected data, and validation metrics are discussed. This paper provides a baseline for the development of a S&C condition monitoring system and overviews techniques for analysis of large amounts of data collected by automatic sensory systems.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 1 PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 5.7 Condition Monitoring and Trend Analysis of Railway Turnouts Based on In-Situ Accelerometer Measurements R. Krc¹, R. Ambur², Z. Hadas³, O. Olaby², I. Vukusic^1,4, O. Plasek¹, M. Entezami² and R. Dixon² ¹Faculty of Civil Engineering, Brno University of Technology, Czech Republic ²Birmingham Centre for Railway Research and Education, University of Birmingham, United Kingdom ³Faculty of Mechanical Engineering, Brno University of Technology, Czech Republic ⁴Vyzkumny Ustav Zeleznicni, a.s., Czech Republic doi:10.4203/ccc.1.5.7 Full Bibliographic Reference for this paper R. Krc, R. Ambur, Z. Hadas, O. Olaby, I. Vukusic, O. Plasek, M. Entezami, R. Dixon, "Condition Monitoring and Trend Analysis of Railway Turnouts Based on In-Situ Accelerometer Measurements", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 5.7, 2022, doi:10.4203/ccc.1.5.7 Keywords: railway, switches and crossings, accelerometer sensors, trend analysis, predictive maintenance. Abstract High dynamic forces at railway switches and crossings (S&C) are the primary cause of frequent defect formation. Regular acquisition of onsite sensory data aids condition evaluation or maintenance planning, which subsequently mitigates problems of unexpected malfunction of S&C components. Accelerometer data collected by in-situ sensors in UK and Czech Republic were used in this research for defining important metrics and validating prediction models. A number of metrics can be calculated from collected signals to provide information about the condition of S&C and its components. Change of these parameters over time is revealed by trend analysis and may signalize increased material deterioration or formation of a defect. Trend analysis methods span from simple regression to more advanced machine learning models for time series prediction and are listed in this paper. Evaluation of proposed models is performed on collected data, and validation metrics are discussed. This paper provides a baseline for the development of a S&C condition monitoring system and overviews techniques for analysis of large amounts of data collected by automatic sensory systems. download the full-text of this paper (PDF, 10 pages, 909 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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