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A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance
Thesis (MEng)--Stellenbosch University, 2021.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2021
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| _version_ | 1867614109713825792 |
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| access_status_str | Open Access |
| author | Van Schalkwyk, Johannes Wallace |
| author2 | Jooste, Johannes Lodewyk |
| author_browse | Jooste, Johannes Lodewyk Van Schalkwyk, Johannes Wallace |
| author_facet | Jooste, Johannes Lodewyk Van Schalkwyk, Johannes Wallace |
| author_sort | Van Schalkwyk, Johannes Wallace |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2021. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/109992 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:46:49.061Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/109992 A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance Van Schalkwyk, Johannes Wallace Jooste, Johannes Lodewyk Lucke, Dominik Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Technology selection framework Acquisition of data sets Predictive maintenance UCTD Maintainability (Engineering) Railroads -- Maintenance Thesis (MEng)--Stellenbosch University, 2021. ENGLISH ABSTRACT: Digital technological advancements is a product of the continuous changes experienced by the technology sector, whereas the advancements are represented as new and emerging technologies. However, to utilise these emerging technologies, new management strategies must be adopted. This is the case for the emergence of a predictive maintenance strategy due to the improved capabilities of the maintenance equipment. Against the background of railway infrastructure maintenance, the problem is that maintenance managers have not leveraged the full potential of emerging data acquisition technologies to support a predictive maintenance approach. This research investigates railway infrastructure maintenance and the technologies capable of acquiring the condition monitoring data to determine why the full potential of the technologies have not been leveraged. It is found that the slow adoption is largely attributed to uncertainty based on technological capabilities, potential barriers and challenges experienced throughout the process. To address the problem in this research, it is decided to develop a framework that supports railway operators with the process of data acquisition technology identi cation, evaluation, and acquisition. The framework ultimately aids railway operators with the shift towards a predictive maintenance approach. This research is based on a mixed method exploratory sequential design methodology, as described by Creswell. The rst phase of this research is responsible for contextualising the problem, and in the second phase, the framework is developed. The completion of the two phases relied on systematic literature reviews, railway industry expert feedback obtained from survey questionnaires, and face validation interviews conducted with railway industry practitioners. Utilising the research approach, as described previously, the Railway Infrastructure Technology Selection Support (RITSS) framework is developed. The RITSS framework consists of three stages namely; Stage 1 mapping assets and technology, Stage 2 technology evaluation, and Stage 3 acquisition mode guide. These stages try and support railway operators to leverage the full potential of emerging data acquisition technologies. Face validations are used to test the real-world applicability of the RITSS framework; in other words, the frameworks feasibility, usability, and utility are assessed by railway industry practitioners. The thesis concludes with a summary of the research conducted, the research objectives achieved, the expected research contributions, the research limitations identi ed, and the recommendations for future research. The overall opinion is that the RITSS framework has enormous potential in the railway infrastructure maintenance sector; however, certain aspects are identi ed that require re nement, such as the acquisition mode guide (Stage 3). AFRIKAANSE OPSOMMING: Digitale tegnologiese vooruitgang is 'n produk van die voortdurende veranderinge wat die tegnologie sektor ervaar, terwyl die vooruitgang as nuwe en opkomende tegnologieë voorgestel word. Om hierdie opkomende tegnologieë te benut, moet daar egter nuwe bestuurstrategieë aanvaar word. Dit is die geval met die ontstaan van 'n voorspellende instandhoudingstrategie as gevolg van die verbeterde vermoëns van die instandhoudingstoerusting. Teen die agtergrond van die instandhouding van die spoorweginfrastruktuur is die probleem dat instandhoudingsbestuurders nie die volle potensiaal van opkomende tegnieke vir die verkryging van data benut om 'n voorspellende instandhoudingsbenadering te ondersteun nie. Hierdie navorsing ondersoek instandhouding van spoorweginfrastruktuur en die tegnologieë wat die toestandmoniteringsdata kan bekom om te bepaal waarom die volle potensiaal van die tegnologieë nie benut word nie. Daar word bevind dat die stadige aanvaarding grotendeels toegeskryf word aan onsekerheid gebaseer op tegnologiese vermoëns, potensiële hindernisse en uitdagings wat gedurende die proses ervaar word. Om die probleem in hierdie navorsing aan te spreek, word daar 'n raamwerk ontwikkel wat spoorwegoperateurs ondersteun met die proses van identi sering, evaluering en verkryging van data-verkrygings tegnologieë. Die raamwerk help spoorwegoperateurs met die skuif na 'n voorspellende instandhoudingsbenadering. Hierdie navorsing is gebaseer op 'n mixed method exploratory sequential design metodologie, soos beskryf deur Creswell. Die eerste fase van hierdie navorsing is verantwoordelik vir die kontekstualisering van die probleem, en in die tweede fase word die raamwerk ontwikkel. Die voltooïng van die twee fases was afhanklik van sistematiese literatuurstudies, terugvoering van kundige spoorwegbedrywe verkry uit vraelyste en Gesigsvalidasies onderhoude wat met spoorwegondernemings gevoer is. Met behulp van die navorsingsbenadering, soos voorheen beskryf, word die RITSS-raamwerk (Railway Infrastructure Technology Selection Support) ontwikkel. Die RITSS-raamwerk bestaan uit drie fases naamlik; Fase 1 kartering van bates en tegnologie, Fase 2 tegnologiese evaluering en Fase 3 gids vir verkrygingsmodus.Hierdie fases probeer spoorwegoperateurs ondersteun om die volle potensiaal van opkomende data-verkrygingstegnologieë te benut. Gesigsvalidasies word gebruik om die toepaslikheid van die RITSS-raamwerk te toets; Met ander woorde, die raamwerk se haalbaarheid, bruikbaarheid en nut word deur die spoorwegbedryf beoordeel. Die proefskrif word afgesluit met 'n samevatting van die navorsing wat gedoen is, die navorsingsdoelstellings wat bereik is, die verwagte bydraes, die navorsingsbeperkings wat geïdenti seer is en die aanbevelings vir toekomstige navorsing. Die algemene tendens is dat die RITSS-raamwerk enorme potensiaal het in die onderhoudsektor vir spoorweginfrastruktuur; sekere aspekte word egter geïdenti seer wat verfyning vereis, soos die gids vir die verkrygingsmodus (fase 3). Masters 2021-02-12T10:58:56Z 2021-04-21T14:35:21Z 2021-02-12T10:58:56Z 2021-04-21T14:35:21Z 2021-03 Thesis http://hdl.handle.net/10019.1/109992 en_ZA Stellenbosch University 258 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Technology selection framework Acquisition of data sets Predictive maintenance UCTD Maintainability (Engineering) Railroads -- Maintenance Van Schalkwyk, Johannes Wallace A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance |
| title | A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance |
| title_full | A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance |
| title_fullStr | A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance |
| title_full_unstemmed | A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance |
| title_short | A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance |
| title_sort | framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance |
| topic | Technology selection framework Acquisition of data sets Predictive maintenance UCTD Maintainability (Engineering) Railroads -- Maintenance |
| url | http://hdl.handle.net/10019.1/109992 |
| work_keys_str_mv | AT vanschalkwykjohanneswallace aframeworkforselectingdataacquisitiontechnologiesinsupportofrailwayinfrastructurepredictivemaintenance AT vanschalkwykjohanneswallace frameworkforselectingdataacquisitiontechnologiesinsupportofrailwayinfrastructurepredictivemaintenance |