Marzia, A., Cremona, M.A., Liu, B. et al. (3 more authors) (2016) Predicting Railway Wheel Wear under Uncertainty of Wear Coefficient, using Universal Kriging. Reliability Engineering and System Safety, 154. pp. 49-59. ISSN 1879-0836
Abstract
Railway wheel wear prediction is essential for reliability and optimal maintenance strategies of railway systems. Indeed, an accurate wear prediction can have both economic and safety implications. In this paper we propose a novel methodology, based on Archard's equation and a local contact model, to forecast the volume of material worn and the corresponding wheel remaining useful life (RUL). A universal kriging estimate of the wear coefficient is embedded in our method. Exploiting the dependence of wear coefficient measurements with similar contact pressure and sliding speed, we construct a continuous wear coefficient map that proves to be more informative than the ones currently available in the literature. Moreover, this approach leads to an uncertainty analysis on the wear coefficient. As a consequence, we are able to construct wear prediction intervals that provide reasonable guidelines in practice.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier Ltd. This is an author produced version of a paper subsequently published in Reliability Engineering & System Safety. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Wear prediction; Wear coefficient; Universal kriging; Remaining useful life |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Jun 2016 11:41 |
Last Modified: | 01 Jul 2017 07:35 |
Published Version: | http://dx.doi.org/10.1016/j.ress.2016.05.012 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.ress.2016.05.012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100755 |