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Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Switch Failures

Narezo Guzman, Daniela and Hadzic, Edin and Baasch, Benjamin and Heusel, Judith and Neumann, Thorsten and Schrijver, Gerrit and Buursma, Douwe and Groos, Jörn Christoffer (2019) Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Switch Failures. In: COMADEM 2019 Proceedings, pp. 1-11. Springer. COMADEM conference 2019, 3.-5. Sep. 2019, Huddersfield, United Kingdom.

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Abstract

Railway switches are a crucial asset since they enable trains to change tracks without stopping. Switch failures can compromise a larger part of the railway infrastructure, which can have a negative impact on reputation and revenues. Switches are a costly asset due to frequent inspections, maintenance and renewal of components. Therefore knowing current and future asset condi-tion can be helpful in optimizing switch maintenance to prevent complete failure. The goal of the research presented here is to exploit switch condition monitoring and weather data to identify switch failures on an early stage. Approaches for detection of anomalous switch behavior and prediction of failures are developed. To validate the anomaly detection results obtained by applying the Isolation Forest algorithm, two different annotated data sets are considered. It is found that the anomaly detection approach performs well when applied to a switch, which is characterized by narrow feature distributions within temperature bins. Moreover first results from an Autoregressive Integrated Moving Average model for failure evolution prediction are presented.

Item URL in elib:https://elib.dlr.de/127352/
Document Type:Conference or Workshop Item (Speech)
Title:Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Switch Failures
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
Baasch, BenjaminBenjamin.Baasch (at) dlr.deUNSPECIFIED
Heusel, JudithJudith.Heusel (at) dlr.deUNSPECIFIED
Neumann, ThorstenThorsten.Neumann (at) dlr.dehttps://orcid.org/0000-0002-9236-0585
Schrijver, GerritUNSPECIFIEDUNSPECIFIED
Buursma, DouweUNSPECIFIEDUNSPECIFIED
Groos, Jörn ChristofferJoern.Groos (at) dlr.dehttps://orcid.org/0000-0003-3871-0756
Date:2019
Journal or Publication Title:COMADEM 2019 Proceedings
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-11
Editors:
EditorsEmailEditor's ORCID iD
Ball, Andrew D.UNSPECIFIEDUNSPECIFIED
Gelman, Len M.UNSPECIFIEDUNSPECIFIED
Rao, Raj B.K.N.UNSPECIFIEDUNSPECIFIED
Publisher:Springer
Series Name:Lecture Notes in Mechanical Engineering
Status:Published
Keywords:Condition Monitoring, Asset Management, Signal Processing
Event Title:COMADEM conference 2019
Event Location:Huddersfield, United Kingdom
Event Type:international Conference
Event Dates:3.-5. Sep. 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Energie und Verkehr
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Data Management and Knowledge Discovery
Deposited By: Narezo Guzman, Daniela
Deposited On:21 Nov 2019 08:48
Last Modified:20 Jun 2021 15:52

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