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A Wear Detection Parameter for the Wheel–Rail Contact Based on Emitted Noise
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Elements. KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Tribologi. (System and Component Design)ORCID iD: 0000-0003-4447-3363
Tyréns AB, Stockholm, Sweden.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Tribologi. (SKD)ORCID iD: 0000-0001-7560-6232
Stockholm County Council, Traffic Administration, Stockholm, Sweden.
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2019 (English)In: Tribology Transactions, ISSN 1040-2004, E-ISSN 1547-397X, Vol. 62, no 3, p. 496-503Article in journal (Refereed) Published
Abstract [en]

This article describes how a wheel–rail wear detection parameter based on noise was developed by running a rapid transit train instrumented with microphones in a depot with a small curve radius. This full-scale test made use of previous knowledge gained from lab-scale tests. The lab- and full-scale tests showed that noise could be used as an indicator of wear transition, when normal wear turns into severe or catastrophic wear. This transition was accompanied by a significant increase in sound pressure and a broader sound pressure amplitude spectrum; that is, a narrow initial amplitude distribution in the normal wear regime and a broader distribution in the severe wear regime. The measured noise from the inner/low and outer/high rails was analyzed in conjunction with the wear from the outer wheel and comparisons were made between cleaned (exposed to severe wear) and lubricated (no wear) rail. Based on those analyses, a wear detection parameter criterion was developed for this specific train. This wear parameter has been implemented in a real-time condition monitoring system so that warnings of the risk for severe wear could be sent to the maintenance department. Validation of the wear parameter and challenges linked to open system conditions are discussed in this article.

Place, publisher, year, edition, pages
2019. Vol. 62, no 3, p. 496-503
Keywords [en]
Railway, noise, condition monitoring, wear, wheel–rail contact
National Category
Tribology (Interacting Surfaces including Friction, Lubrication and Wear) Vehicle Engineering
Research subject
The KTH Railway Group - Tribology; Machine Design
Identifiers
URN: urn:nbn:se:kth:diva-249942DOI: 10.1080/10402004.2019.1576957ISI: 000466145500001Scopus ID: 2-s2.0-85064477098OAI: oai:DiVA.org:kth-249942DiVA, id: diva2:1306359
Funder
EU, FP7, Seventh Framework Programme
Note

QC 20190522

Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-05-22Bibliographically approved

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