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An unsupervised machine learning approach to extract wheel and track health status indicators from train-borne accelerometer data

Baasch, Benjamin and Roth, Michael Helmut and Schulz, Sebastian and Groos, Jörn Christoffer (2020) An unsupervised machine learning approach to extract wheel and track health status indicators from train-borne accelerometer data. ESREL2020 PSAM15, 01. – 05. Nov. 2020, Venedig, Italien. ISBN 978-981-14-8593-0.

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Official URL: https://www.rpsonline.com.sg/proceedings/esrel2020/html/3898.xml


Item URL in elib:https://elib.dlr.de/134005/
Document Type:Conference or Workshop Item (Speech)
Title:An unsupervised machine learning approach to extract wheel and track health status indicators from train-borne accelerometer data
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Baasch, BenjaminBenjamin.Baasch (at) dlr.dehttps://orcid.org/0000-0003-1970-3964
Roth, Michael HelmutM.Roth (at) dlr.deUNSPECIFIED
Schulz, SebastianS.Schulz (at) dlr.deUNSPECIFIED
Groos, Jörn ChristofferJoern.Groos (at) dlr.dehttps://orcid.org/0000-0003-3871-0756
Date:November 2020
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
ISBN:978-981-14-8593-0
Status:Published
Keywords:Machine Learning, Dimensionality Reduction, Blind Signal Separation, Condition Monitoring, Condition-Based Maintenance, Axle-Box Acceleration
Event Title:ESREL2020 PSAM15
Event Location:Venedig, Italien
Event Type:international Conference
Event Dates:01. – 05. Nov. 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Rail Transport
DLR - Research area:Transport
DLR - Program:V SC Schienenverkehr
DLR - Research theme (Project):V - Digitalisierung und Automatisierung des Bahnsystems
Location: Berlin-Adlershof , Braunschweig
Institutes and Institutions:Institute of Transportation Systems
Institute of Transportation Systems > Data Management and Knowledge Discovery
Deposited By: Baasch, Benjamin
Deposited On:16 Nov 2020 10:23
Last Modified:20 Jun 2021 15:53

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