Condition monitoring and trend analysis of railway turnouts based on in-situ accelerometer measurements

Rostislav Krč, Ramakrishnan Ambur, Zdeněk Hadaš, Osama Olaby, Ivan Vukušič, Otto Plasek, Mani Entezami, Roger Dixon

Research output: Contribution to conference (unpublished)Otherpeer-review

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.
Original languageEnglish
Number of pages8
Publication statusPublished - 24 Aug 2022
Event5th International Conference on Railway Technology - Montpellier, France
Duration: 22 Aug 202225 Aug 2022
https://www.railwaysconference.com/

Conference

Conference5th International Conference on Railway Technology
Abbreviated titleRailways 2022
Country/TerritoryFrance
CityMontpellier
Period22/08/2225/08/22
Internet address

Keywords

  • Railway Switches and Crossings
  • Accelerometer Sensors
  • Trend Analysis
  • Predictive Maintenance

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