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A maintenance decision support digital twin for passenger trainset life cycle costing
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_ | 1867613790900584448 |
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| access_status_str | Open Access |
| author | Kirkman, Abbey Lea |
| author2 | Basson, Anton |
| author_browse | Basson, Anton Kirkman, Abbey Lea |
| author_facet | Basson, Anton Kirkman, Abbey Lea |
| author_sort | Kirkman, Abbey Lea |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2025. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/134660 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:41:45.229Z |
| 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:10019.1/134660 A maintenance decision support digital twin for passenger trainset life cycle costing Kirkman, Abbey Lea Basson, Anton Bekker, Annie Stellenbosch University. Faculty of Engineering. Dept. of Mechanical & Mechatronic Engineering. Digital twins (Computer simulation) Railroads -- Rolling stock Passenger trains -- Maintenance and repair Industrial management -- Decision making Thesis (MEng)--Stellenbosch University, 2025. Kirkman, A. L. 2025. A Maintenance Decision Support Digital Twin for Passenger Trainset Life Cycle Costing. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/28564441-8830-4e35-bf6a-927fe6b5e960 ENGLISH ABSTRACT: Digital twin (DT) technology has the potential to assist the railway industry through data-led optimisation of maintenance, improving overall operational performance, and reducing life cycle costs (LCC). However, limited research exists on a comprehensive DT architecture that integrates LCC, maintenance, and operational data throughout the entire life cycle of a fleet of railway assets. The objective of this thesis is the development of a proof-of-concept DT system that, using passenger trainsets’ operational and maintenance-related data, as well as LCC models, will support maintenance decisions. The scope encompasses planned maintenance considerations, including both preventive and corrective maintenance, for a fleet of trainsets built to a set of baselines. The system enables analysis from a fleet-wide level down to individual components and facilitates analysis of differences between baselines, trainsets, subsystems, and components. The DT architecture presented in the thesis builds on a service-oriented reference architecture and incorporates a hierarchical structure to represent the railway fleet environment. In this hierarchy, type digital twin aggregates (TDTAs) and DTs are created for trainsets, their subsystems, individual components, maintenance processes, and resources across different baselines. TDTAs are introduced to group functionally similar subsystems and components to handle the complexity of managing large numbers of trainsets with varying configurations. A proof-of-concept implementation of the DT architecture was developed for a railway industry case study focusing on passenger trainset LCC. The implementation demonstrates the DT architecture's ability to ingest data from external sources, perform data transformations, and integrate baseline, trainset, subsystem, and component instances into the DT hierarchy. The implementation could perform LCC analyses and facilitate cost comparisons across different baselines, trainsets, subsystems, and components. Additionally, maintenance decision support was provided through what-if analysis capabilities, enabling stakeholders to evaluate the cost implications of different maintenance plans. The evaluation demonstrates that the use of aggregation based on TDTAs was key to addressing scalability constraints in railway fleet management, enabling the architecture to manage large numbers of trainsets. The service-oriented design provides modularity that supports independent component development and system evolution. The hierarchical organisation from baseline to component level enables both fleet-wide analysis and detailed component-level decision support. However, the evaluation reveals some architectural trade-offs, including sequential processing constraints due to the data loading strategies and scalability limitations in concurrent request handling. This work addresses a significant gap in DT applications for railway asset management, particularly the integration of DT technology with LCC for comprehensive fleet management. This research demonstrates how DT aggregation principles can transition railway operators from fragmented, baseline-specific maintenance analysis to integrated fleet-wide decision support, potentially improving maintenance efficiency and reducing LCC through better-informed maintenance planning decisions across a fleet of trainsets. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Masters 2025-12-22T10:18:07Z 2025-12-22T10:18:07Z 2025-12 Thesis https://scholar.sun.ac.za/handle/10019.1/134660 en Stellenbosch University xiii, 96 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Digital twins (Computer simulation) Railroads -- Rolling stock Passenger trains -- Maintenance and repair Industrial management -- Decision making Kirkman, Abbey Lea A maintenance decision support digital twin for passenger trainset life cycle costing |
| title | A maintenance decision support digital twin for passenger trainset life cycle costing |
| title_full | A maintenance decision support digital twin for passenger trainset life cycle costing |
| title_fullStr | A maintenance decision support digital twin for passenger trainset life cycle costing |
| title_full_unstemmed | A maintenance decision support digital twin for passenger trainset life cycle costing |
| title_short | A maintenance decision support digital twin for passenger trainset life cycle costing |
| title_sort | maintenance decision support digital twin for passenger trainset life cycle costing |
| topic | Digital twins (Computer simulation) Railroads -- Rolling stock Passenger trains -- Maintenance and repair Industrial management -- Decision making |
| url | https://scholar.sun.ac.za/handle/10019.1/134660 |
| work_keys_str_mv | AT kirkmanabbeylea amaintenancedecisionsupportdigitaltwinforpassengertrainsetlifecyclecosting AT kirkmanabbeylea maintenancedecisionsupportdigitaltwinforpassengertrainsetlifecyclecosting |