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An on-board track monitoring system for modern railway vehicles
Thesis (MEng)--Stellenbosch University, 2025.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867613906118115328 |
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| access_status_str | Open Access |
| author | Du Toit, Daniel |
| author2 | Bekker, Annie |
| author_browse | Bekker, Annie Du Toit, Daniel |
| author_facet | Bekker, Annie Du Toit, Daniel |
| author_sort | Du Toit, Daniel |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2025. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/134609 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:43:35.067Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/134609 An on-board track monitoring system for modern railway vehicles Du Toit, Daniel Bekker, Annie Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering. Railroads -- Automation Kalman filtering Railroads -- Maintenance and repair Railroad engineering Thesis (MEng)--Stellenbosch University, 2025. Du Toit, D. 2025. An On-board Track Monitoring System for Modern Railway Vehicles. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/677be43f-541d-450d-8755-fda79afdd685 ENGLISH ABSTRACT: Effective monitoring of the railway infrastructure is essential to ensure oper-ational safety, reliability, and efficient asset management. In recent decades, South Africa’s rail industry has faced significant challenges, prompting the need for improved monitoring practices. However, recent efforts by Gibela Rail Transport Consortium have led to a revival of the passenger rail sec-tor through the introduction of a modern, state-of-the-art rail fleet. As this modern fleet is introduced, the reliability and safety of railway operations be-come increasingly dependent on effective track monitoring and maintenance strategies. Traditional methods, reliant on periodic assessments by specialised vehicles, are expensive and infrequent, highlighting the opportunity for contin-uous, on-board monitoring systems. This research presents the development of a digital on-board track monitoring system for modern passenger trains, leveraging Industry 4.0 technologies to automate the identification of track geometry defects and support condition-based maintenance strategies. The system integrates data from tri-axial axle-box accelerometers, a bogie-mounted Inertial Measurement Unit, and a Global Positioning System. Pre-processing techniques, including digital filtering and virtual sensing, are used to estimate angular accelerations from the axle-box signals, which are subse-quently fused with inertial measurements using a Kalman filter. The resulting bogie motion estimates enable the derivation of track geometry parameters, such as longitudinal level, horizontal alignment, cross-level, and twist. How-ever, gauge could not be measured with the selected sensor configuration, as accurate gauge estimation typically requires optical or laser-based systems. Defects are identified using the mid-chord deviation method and classified based on standards from Transnet’s manual for track maintenance. This core algorithm is integrated into a complete digital monitoring platform that in-cludes an SQLite database for data storage, a graphical user interface for near real-time visualisation, automatic defect alerts, and a report generator that compares present and past track conditions. The system’s performance was evaluated by simulating its functionality with real-world data collected from an X’Trapolis Mega passenger train over a dis-tance of 25 km between Cape Town and Fish Hoek. The simulation demon-strated the system’s ability to identify and classify A-, B-, and C-standard defects along the route in near real-time. This research establishes a successful proof of concept for an automated on-board track monitoring system that can enhance railway asset management. By enabling more frequent monitoring, this system offers clear advantages over scheduled inspections and contributes to the advancement of intelligent railway monitoring systems, promoting safer and more efficient operations. Future work will focus on formal validation against certified track measurement vehicles, pilot deployment on in-service trains, and the integration of non-contact sensors for complete five-parameter track geometry assessment. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Masters 2025-12-18T10:42:01Z 2025-12-18T10:42:01Z 2025-12 Thesis https://scholar.sun.ac.za/handle/10019.1/134609 en Stellenbosch University xiv, 127 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Railroads -- Automation Kalman filtering Railroads -- Maintenance and repair Railroad engineering Du Toit, Daniel An on-board track monitoring system for modern railway vehicles |
| title | An on-board track monitoring system for modern railway vehicles |
| title_full | An on-board track monitoring system for modern railway vehicles |
| title_fullStr | An on-board track monitoring system for modern railway vehicles |
| title_full_unstemmed | An on-board track monitoring system for modern railway vehicles |
| title_short | An on-board track monitoring system for modern railway vehicles |
| title_sort | on board track monitoring system for modern railway vehicles |
| topic | Railroads -- Automation Kalman filtering Railroads -- Maintenance and repair Railroad engineering |
| url | https://scholar.sun.ac.za/handle/10019.1/134609 |
| work_keys_str_mv | AT dutoitdaniel anonboardtrackmonitoringsystemformodernrailwayvehicles AT dutoitdaniel onboardtrackmonitoringsystemformodernrailwayvehicles |