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Detecting singular track defects by time-frequency signal separation of axle-box acceleration data

Baasch, Benjamin und Groos, Jörn Christoffer und Roth, Michael Helmut und Havrila, Patrik (2019) Detecting singular track defects by time-frequency signal separation of axle-box acceleration data. In: World Congress on Railway Research (WCRR) 2019, Tokyo, Japan. 12th World Congress on Railway Research, 2019-10-28 - 2019-11-01, Tokyo, Japan.

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Kurzfassung

Singular railway track irregularities, such as squats and corrugation have a major impact on the ride stability, noise emission, comfort and safety of freight and passenger trains. Therefore, the detection and monitoring of such defects play an important role in railway track maintenance. Embedded low-cost sensors on in-service vehicles provide the opportunity of quasi-continuous condition monitoring of railway tracks and can thus enhance existing track maintenance strategies. In this paper we demonstrate a processing sequence to detect singular track defects from noisy axle-box acceleration (ABA) data. The data are acquired with a multi-sensor prototype measurement system on a shunter locomotive operating on the industrial railway network of the inland harbor of Braunschweig (Germany). A blind signal separation (BSS) algorithm based on non-negative matrix factorization is applied to the ABA data in the time-frequency domain. It is completely data-driven and hence does not rely on a priori knowledge or physical models. The algorithm makes use of different time-frequency characteristics of the signal components and is thus able to separate quasi-continuous band-limited signal components from transient broad-band components. The magnitude of the transient components reflects the strength of track singularities along the track and can hence be used to detect and quantify short track defects. Through georeferencing the identified defects can be localized, mapped on the track and be used to guide specific maintenance actions. Additionally, the BSS algorithm shows the potential to reduce the dimensionality of the data without significant loss of information.

elib-URL des Eintrags:https://elib.dlr.de/121517/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Detecting singular track defects by time-frequency signal separation of axle-box acceleration data
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Baasch, BenjaminBenjamin.Baasch (at) dlr.dehttps://orcid.org/0000-0003-1970-3964NICHT SPEZIFIZIERT
Groos, Jörn ChristofferJoern.Groos (at) dlr.dehttps://orcid.org/0000-0003-3871-0756NICHT SPEZIFIZIERT
Roth, Michael HelmutM.Roth (at) dlr.dehttps://orcid.org/0000-0002-4812-346XNICHT SPEZIFIZIERT
Havrila, PatrikPatrik.Havrila (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2019
Erschienen in:World Congress on Railway Research (WCRR) 2019, Tokyo, Japan
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:condition monitoring, blind signal separation, axle box acceleration, track defects, dimensionality reduction
Veranstaltungstitel:12th World Congress on Railway Research
Veranstaltungsort:Tokyo, Japan
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:28 Oktober 2019
Veranstaltungsende:1 November 2019
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Schienenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V SC Schienenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - Digitalisierung und Automatisierung des Bahnsystems (alt)
Standort: Berlin-Adlershof
Institute & Einrichtungen:Institut für Verkehrssystemtechnik
Institut für Verkehrssystemtechnik > Datenerfassung und Informationsgewinnung
Hinterlegt von: Baasch, Dr. Benjamin
Hinterlegt am:08 Jan 2020 08:30
Letzte Änderung:24 Apr 2024 20:25

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