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Anomaly detection for railway switch monitoring data to enable condition-based maintenance

Narezo Guzman, Daniela und Hadzic, Edin und Groos, Jörn Christoffer (2019) Anomaly detection for railway switch monitoring data to enable condition-based maintenance. In: WCRR 2019 Congress Proceedings. 12th World Congress on Railway Research, 2019-10-28 - 2019-11-01, Tokyo, Japan.

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Kurzfassung

Railway switches are a crucial railway asset since they enable train operators to route trains by changing tracks. A switch failure can compromise the availability of a larger part of the infrastructure in cases when train traffic heavily relies on that switch. Nearly one third of the total costs of railway maintenance is spent for switches and crossings. Thus there is a need for increased switch reliability and costs reduction. Nowadays tens of thousands assets around the world are remotely monitored. Switch monitoring data in combination with weather information can be combined to characterize switch functioning and to determine how it is influenced by the weather. Such a characterization allows the detection of anomalies i.e. emerging and sudden failures under different weather conditions. In this paper results from data-based switch-specific models for anomaly detection, which account for temperature influence, are presented. These results are validated against two annotated data sets based on experts' assessment. It is found that the model capabilities strongly depend from switch to switch. A model trained with features (derived from the monitoring data) that are narrowly distributed within small temperature intervals has a very good performance; otherwise the performance is poor. Additionally the influence of rain and humidity on the switch functioning was explored by including data from the closest weather station. No correlation was found probably due to the fact that the available weather information is only a proxy of the local conditions at the switch, stressing the importance of measuring weather parameters at the asset.

elib-URL des Eintrags:https://elib.dlr.de/121295/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Anomaly detection for railway switch monitoring data to enable condition-based maintenance
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Narezo Guzman, DanielaDaniela.NarezoGuzman (at) dlr.dehttps://orcid.org/0000-0001-9748-1354NICHT SPEZIFIZIERT
Hadzic, EdinEdin.Hadzic (at) strukton.comNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Groos, Jörn ChristofferJoern.Groos (at) dlr.dehttps://orcid.org/0000-0003-3871-0756NICHT SPEZIFIZIERT
Datum:2019
Erschienen in:WCRR 2019 Congress Proceedings
Referierte Publikation:Nein
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Condition-based maintenance, switch condition monitoring
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:Verkehrsmanagement (alt)
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V VM - Verkehrsmanagement
DLR - Teilgebiet (Projekt, Vorhaben):V - Next Generation Railway Systems III (alt)
Standort: Berlin-Adlershof
Institute & Einrichtungen:Institut für Verkehrssystemtechnik > Datenerfassung und Informationsgewinnung
Hinterlegt von: Narezo Guzman, Daniela
Hinterlegt am:07 Okt 2019 08:43
Letzte Änderung:24 Apr 2024 20:25

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