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Improved Railway Track Irregularities Classification by a Model Inversion Approach

Schenkendorf, René and Dutschk, Beate and Lüddecke, Katrin and Groos, Jörn Christoffer (2016) Improved Railway Track Irregularities Classification by a Model Inversion Approach. In: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016, pp. 62-69. Prognostics and Health Management Society. Third European Conference of the Prognostics and Health Management Society 2016, 05.-08. Jul. 2016, Bilbao, Spanien. ISBN 9781936263219

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Official URL: https://www.phmsociety.org/events/conference/europhm/16/proceedings

Abstract

Over time railway networks have become complex Systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures are made up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenance strategy leading to an optimized scheduling of maintenance actions taking the actual aswell as the expected future infrastructure condition into account. A prerequisite is, however, that the thousands of Kilometers of railway tracks are almost continuously monitored. Thus, a promising approach is the usage of low-cost sensors, e.g. accelerometers and gyroscopes, which can be installed on common in-line freight and passenger trains. Due to ambiguous data records a credible classification of railway track irregularities directly from these data is challenging. Alternatively to this pure data-driven approach, in this paper a novel hybrid Approach is presented. To this end, a simplified vehicle Suspension model is applied for the purpose of railway track condition monitoring by analyzing the dynamic railway track - Train interactions. The inversion of the model can be used to recalculate the actual inputs (irregularities) of the monitored system (rail surface) which have caused recorded System Responses (dynamic vehicle reactions and acceleration data, respectively). These recalculated inputs are a sound Basis of subsequent data-driven condition monitoring analyses. In this preliminary study, a classification algorithm is implemented to identify a simulated railway track irregularity automatically.

Item URL in elib:https://elib.dlr.de/105383/
Document Type:Conference or Workshop Item (Speech)
Title:Improved Railway Track Irregularities Classification by a Model Inversion Approach
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Schenkendorf, Renérene.schenkendorf (at) dlr.deUNSPECIFIED
Dutschk, Beatebeate.dutschk (at) dlr.deUNSPECIFIED
Lüddecke, Katrinkatrin.lueddecke (at) dlr.deUNSPECIFIED
Groos, Jörn ChristofferJoern.Groos (at) dlr.dehttps://orcid.org/0000-0003-3871-0756
Date:July 2016
Journal or Publication Title:Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 62-69
Editors:
EditorsEmail
Eballard, IoanaUNSPECIFIED
Bregon, AnibalUNSPECIFIED
Publisher:Prognostics and Health Management Society
ISBN:9781936263219
Status:Published
Keywords:Condition Monitoring, Railway, Acceleration, Vibration, Inversion, Model
Event Title:Third European Conference of the Prognostics and Health Management Society 2016
Event Location:Bilbao, Spanien
Event Type:international Conference
Event Dates:05.-08. Jul. 2016
Organizer:Prognostics and Health Management Society
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 - TrackScan (old)
Location: Braunschweig
Institutes and Institutions:Institute of Transportation Systems
Institute of Transportation Systems > Railway System
Institute of Transportation Systems > Bahntechnik
Deposited By: Groos, Jörn Christoffer
Deposited On:22 Jul 2016 17:35
Last Modified:31 Jul 2019 20:02

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