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Measurement Systems Analysis of Railway Measurement Cars
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-3911-8009
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
2016 (English)Conference paper, Presentation (Refereed)
Abstract [en]

Purpose: The presentation proposes ways to understand and quantify the variation component due to the measurement system of railway track properties using subsequent runs from measurement cars.Background: Railway infrastructure conditions are commonly inspected by using measurement cars. The measurements are performed with some regularity, and the inspection frequencies could for instance be set taking into account the common train axle loads, railway speed or load bearing classification, number of trains passing, the known railway condition, or the availability of the measurement cars. By combining different inspections of the same track section, it is also possible to monitor the degradation of the infrastructure over time. Often, the railway system is inspected by many measurement cars, and for single tracks, measurements can be obtained from the car travelling in different directions. The measurements are performed at different speeds, related to random variation, but also to the maximum speeds at which the measurement cars operate. The measurements are also afflicted by external variation sources, some of which are acting with a known direction, such as the wear of the track which increases property variation. Maintenance usually (but not always) result in reduced property variation, whereas other sources such as climate related properties such as spring thaw may induce variation over time, but also induce variation that show a periodic behavior with periods with increasing as well as decreasing property variation. This presentation aims to devise a model for how these variation sources may be separated, with the main aim to classify measurement error, but also to estimate the magnitude of other variation sources.Method: No statistically significant differences were found between repeated measurements of cars travelling back and forth on the single track found at the Swedish Iron ore line. These measurements contain measurement error as well as error due to short term degradation and variation due to measurement. As measurement variance is added, it was concluded that the measurement variation could not be larger than the variation shown by repeat measurements. By comparing repeated measurements over time and subtracting variation due to wear, measurement variation for different cars, measurement speeds and measurement directions was estimated using Generalized Linear Models regression analysis. Co-variation between measurement cars and measurement speeds were accounted for using Ridge regression and Elastic Net regression.Results: The regression analysis shows that whereas both measurement speed and the measurement car individuals correlate with the measurement variation obtained, regularized regression points to the measurement cars as the major variation factor and that different measurement cars have different measurement precisionDiscussion and conclusion: The study demonstrates how repeated measurements from regular process data and thus not obtained using the regular and systematic experimental procedures of measurement system analysis can be used for estimation of the variation components of the measurement system. As a side effect, the sizes of other variation sources, external to the measurement system, can be estimated.

Place, publisher, year, edition, pages
2016.
National Category
Reliability and Maintenance
Research subject
Quality Technology and Management; Effective innovation and organisation (AERI); Intelligent industrial processes (AERI); Sustainable transportation (AERI); Enabling ICT (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-40436Local ID: f94ebaca-1c61-47a5-80b6-eb0278051269OAI: oai:DiVA.org:ltu-40436DiVA: diva2:1013958
Conference
International Conference on the Interface between Statistics and Engineering : 20/06/2016 - 23/06/2016
Projects
Förbättrad tillståndsbedömning genom statistisk analys
Note
Godkänd; 2016; 20160701 (bjarne)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-09-15Bibliographically approved

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