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Integration of RAMS in LCC analysis for linear transportinfrastructures: A case study for railways
CEMOSA.
CEMOSA.
CEMOSA.
CEMOSA.
Vise andre og tillknytning
2017 (engelsk)Inngår i: IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, E-ISSN 1757-899X, Vol. 236Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Life-cycle cost (LCC) analysis is an economic technique used to assess the totalcosts associated with the lifetime of a system in order to support decision making in long termstrategic planning. For complex systems, such as railway and road infrastructures, the cost ofmaintenance plays an important role in the LCC analysis. Costs associated with maintenanceinterventions can be more reliably estimated by integrating the probabilistic nature of thefailures associated to these interventions in the LCC models. Reliability, Maintainability,Availability and Safety (RAMS) parameters describe the maintenance needs of an asset in aquantitative way by using probabilistic information extracted from registered maintenanceactivities. Therefore, the integration of RAMS in the LCC analysis allows obtaining reliablepredictions of system maintenance costs and the dependencies of these costs with specific costdrivers through sensitivity analyses. This paper presents an innovative approach for acombined RAMS & LCC methodology for railway and road transport infrastructures beingdeveloped under the on-going H2020 project INFRALERT. Such RAMS & LCC analysisprovides relevant probabilistic information to be used for condition and risk-based planning ofmaintenance activities as well as for decision support in long term strategic investmentplanning.

sted, utgiver, år, opplag, sider
Institute of Physics (IOP), 2017. Vol. 236
HSV kategori
Forskningsprogram
Drift och underhållsteknik; Trafikteknik; Hållbara transporter (FOI)
Identifikatorer
URN: urn:nbn:se:ltu:diva-65561DOI: 10.1088/1757-899X/236/1/012106ISI: 000417428700106OAI: oai:DiVA.org:ltu-65561DiVA, id: diva2:1140009
Konferanse
BESTInfra 2017, Czech Technical University, Prague, Czech Republic, September 21-22 2017
Prosjekter
INFRALERT
Forskningsfinansiär
EU, Horizon 2020, 636496
Merknad

2018-01-09 (andbra);Konferensartikel i tidskrift;Bibliografisk uppgift: This research was carried out within the INFRALERT project. This project has received funding fromthe EU Horizon 2020 research and innovation programme under grant agreement No 636496. Theauthors also thank Trafikverket for providing the data in the case study analysis.

Tilgjengelig fra: 2017-09-11 Laget: 2017-09-11 Sist oppdatert: 2018-11-16bibliografisk kontrollert

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