Synchronization of On-board Track Geometry Monitoring Signals - to Enable Machine Learning Predictions - Abstract

Aug 4, 2026

Upload by Quinn

Accurate detection and prediction of railway track geometry defects underpin safety, reliability, and efficiency in rail operations. In Europe, conventional monitoring relies on costly diagnostic trains that run infrequently compared with commercial traffic. This work proposes a methodology to synchronise onboard monitoring signals from commercial trains using accelerometers, gyroscopes, and GNSS, supporting the data cleansing required for machine learning prediction of track geometry evolution. The approach addresses sensor-placement differences, varying recording start points, and GNSS misalignments (especially in tunnels) via cross-level references and a peak finder algorithm, enabling defect evolution tracking at fixed locations for scalable predictive maintenance.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Railway track monitoring background and TRVs

Limits of diagnostic-train snapshots

Proposed onboard-signal synchronisation approach

University of Birmingham Synchronization of On-board Track Geometry Monitoring Signals to Enable Machine Learning Predictions Abdi Goudarzi, Sepehr; Licciardello, Riccardo; Kaviani, Nadia; Aldin Mansouri, Shahab; Entezami, Mani DOI: 10.1109/EDCC-C66476.2025.00041 License: Creative Commons: Attribution (CC BY) Document Version Peer reviewed version Citation for published version (Harvard): Abdi Goudarzi, S, Licciardello, R, Kaviani, N, Aldin Mansouri, S & Entezami, M 2025, Synchronization of Onboard Track Geometry Monitoring Signals to Enable Machine Learning Predictions. in 2025 20th European Dependable Computing Conference Companion Proceedings (EDCC-C). , 11144825, Institute of Electrical and Electronics Engineers (IEEE), pp. 108-112. [https://doi.org/10.1109/EDCC-C66476.2025.00041](https://doi.org/10.1109/EDCC-C66476.2025.00041) Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate. Download date: 03. Aug. 2026 Synchronisation of on-board track geometry monitoring signals to enable Machine Learning predictions Sepehr Abdi Goudarzi Department of Civil, Building and Environmental Engineering Sapienza University of Rome Rome, Italy [sepehr.abdigoudarzi@uniroma1.it](sepehr.abdigoudarzi@uniroma1.it) Shahab Aldin Mansouri Department of Civil, Building and Environmental Engineering Sapienza University of Rome Rome, Italy [shahabaldin.mansouri@uniroma1.it](shahabaldin.mansouri@uniroma1.it) Riccardo Licciardello Department of Civil, Building and Environmental Engineering Sapienza University of Rome Rome, Italy [riccardo.licciardello@uniroma1.it](riccardo.licciardello@uniroma1.it) Mani Entezami Birmingham Centre for Railway Research and Education University of Birmingham Birmingham, United Kingdom [M.Entezami@bham.ac.uk](M.Entezami@bham.ac.uk) Nadia Kaviani Department of Civil, Building and Environmental Engineering Sapienza University of Rome Rome, Italy [nadia.kaviani@uniroma1.it](nadia.kaviani@uniroma1.it) Abstract— Accurate detection and prediction of railway track geometry defects is critical for ensuring safety, reliability and efficiency in rail operations. In Europe, traditional track geometry monitoring relies heavily on diagnostic trains, which are costly and operate infrequently if compared with commercial trains. This work presents a novel methodology to synchronise signals from onboard monitoring using sensors installed on commercial trains, as a part of the data cleansing needed to prepare for Machine Learning prediction of track geometry evolution. The

FAQs

Why is synchronising onboard track geometry monitoring signals important for machine learning predictions?

Synchronisation aligns multiple recordings despite differences in sensor placement and recording start points, enabling precise tracking of how defects evolve over time at the same locations for reliable predictive maintenance.

What sensors are used to estimate track geometry features in the proposed methodology?

The approach uses accelerometers, gyroscopes, and GNSS to estimate track geometry features from sensors installed on commercial trains.

How does the method overcome GNSS misalignment and tunnel-related challenges?

It introduces a data synchronisation methodology that leverages cross-level references combined with a peak finder algorithm to achieve precise alignment across recordings, including where GNSS misalignments are more problematic in tunnels.

Aug 4, 2026

Upload by Quinn