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Measurement System Analysis of Railway Track Geometry Data using Secondary Data
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.ORCID-id: 0000-0003-3911-8009
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.ORCID-id: 0000-0002-6479-9101
2016 (Svenska)Konferensbidrag, Enbart muntlig presentation (Refereegranskat)
Abstract [en]

In this paper, we use secondary data to make a partial measurement system analysis of railway measurement cars and their obtained track geometry data. When a measurement car passes the same track section shortly after the previous passage, such as returning in the other direction after reaching a railway endpoint, the repeated measurements hold information of the measurement uncertainty of that car. Reasons for the measurement uncertainty can be sought in other variables that also are stored in the database, such as the individual car identity, the type of car, the speed of the car during measurement, and the travelled direction of the car. By also considering other known factors during the time of measurement as regressors, such as ground frost periods, enhanced modelling may be achieved and also indicate if such periods should be avoided to improve the measurement data quality.The results of this study suggest that the type of car had the largest influence on measurement variation out of the studied regressors. If the variation of a track geometry property on a track section is studied, the variation component belonging to the type of car can be deducted, improving data quality. We suggest that the method could also be used to find track sections that are prone to large seasonal variation, such as due to ground frost.

Ort, förlag, år, upplaga, sidor
2016.
Nationell ämneskategori
Tillförlitlighets- och kvalitetsteknik
Forskningsämne
Kvalitetsteknik; Intelligenta industriella processer (FOI); Möjliggörande IKT (FOI); Effektiv innovation och organisation (FOI); Hållbara transporter (FOI)
Identifikatorer
URN: urn:nbn:se:ltu:diva-27782Lokalt ID: 15180d02-51df-4760-83c7-d0c0ff8d94afOAI: oai:DiVA.org:ltu-27782DiVA, id: diva2:1000972
Konferens
eMaintenance 2016 : 15/06/2016 - 16/06/2016
Projekt
Statistiska metoder för förbättring av kontinuerliga tillverkningsprocesser
Anmärkning
Godkänd; 2016; 20160701 (bjarne)Tillgänglig från: 2016-09-30 Skapad: 2016-09-30 Senast uppdaterad: 2018-03-16Bibliografiskt granskad

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Bergquist, BjarneSöderholm, Peter

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