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

Narezo Guzman, Daniela and Hadzic, Edin and 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, 28. Oct - 01. Nov. 2019, Tokyo, Japan.

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Abstract

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.

Item URL in elib:https://elib.dlr.de/121295/
Document Type:Conference or Workshop Item (Speech)
Title:Anomaly detection for railway switch monitoring data to enable condition-based maintenance
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Narezo Guzman, DanielaDaniela.NarezoGuzman (at) dlr.dehttps://orcid.org/0000-0001-9748-1354
Hadzic, EdinEdin.Hadzic (at) strukton.comUNSPECIFIED
Groos, Jörn ChristofferJoern.Groos (at) dlr.dehttps://orcid.org/0000-0003-3871-0756
Date:2019
Journal or Publication Title:WCRR 2019 Congress Proceedings
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Condition-based maintenance, switch condition monitoring
Event Title:12th World Congress on Railway Research
Event Location:Tokyo, Japan
Event Type:international Conference
Event Dates:28. Oct - 01. Nov. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Next Generation Railway Systems III (old)
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Data Management and Knowledge Discovery
Deposited By: Narezo Guzman, Daniela
Deposited On:07 Oct 2019 08:43
Last Modified:20 Jun 2021 15:51

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