elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Detecting singular track defects by time-frequency signal separation of axle-box acceleration data

Baasch, Benjamin and Groos, Jörn Christoffer and Roth, Michael Helmut and Havrila, Patrik (2019) Detecting singular track defects by time-frequency signal separation of axle-box acceleration data. In: World Congress on Railway Research (WCRR) 2019, Tokyo, Japan. 12th World Congress on Railway Research, 28. Okt. - 01. Nov. 2019, Tokyo, Japan.

[img] PDF
344kB

Abstract

Singular railway track irregularities, such as squats and corrugation have a major impact on the ride stability, noise emission, comfort and safety of freight and passenger trains. Therefore, the detection and monitoring of such defects play an important role in railway track maintenance. Embedded low-cost sensors on in-service vehicles provide the opportunity of quasi-continuous condition monitoring of railway tracks and can thus enhance existing track maintenance strategies. In this paper we demonstrate a processing sequence to detect singular track defects from noisy axle-box acceleration (ABA) data. The data are acquired with a multi-sensor prototype measurement system on a shunter locomotive operating on the industrial railway network of the inland harbor of Braunschweig (Germany). A blind signal separation (BSS) algorithm based on non-negative matrix factorization is applied to the ABA data in the time-frequency domain. It is completely data-driven and hence does not rely on a priori knowledge or physical models. The algorithm makes use of different time-frequency characteristics of the signal components and is thus able to separate quasi-continuous band-limited signal components from transient broad-band components. The magnitude of the transient components reflects the strength of track singularities along the track and can hence be used to detect and quantify short track defects. Through georeferencing the identified defects can be localized, mapped on the track and be used to guide specific maintenance actions. Additionally, the BSS algorithm shows the potential to reduce the dimensionality of the data without significant loss of information.

Item URL in elib:https://elib.dlr.de/121517/
Document Type:Conference or Workshop Item (Speech)
Title:Detecting singular track defects by time-frequency signal separation of axle-box acceleration data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Baasch, BenjaminBenjamin.Baasch (at) dlr.deUNSPECIFIED
Groos, Jörn ChristofferJoern.Groos (at) dlr.dehttps://orcid.org/0000-0003-3871-0756
Roth, Michael HelmutM.Roth (at) dlr.deUNSPECIFIED
Havrila, PatrikPatrik.Havrila (at) dlr.deUNSPECIFIED
Date:2019
Journal or Publication Title:World Congress on Railway Research (WCRR) 2019, Tokyo, Japan
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:condition monitoring, blind signal separation, axle box acceleration, track defects, dimensionality reduction
Event Title:12th World Congress on Railway Research
Event Location:Tokyo, Japan
Event Type:international Conference
Event Dates:28. Okt. - 01. Nov. 2019
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
Institutes and Institutions:Institute of Transportation Systems
Institute of Transportation Systems > Data Management and Knowledge Discovery
Deposited By: Baasch, Benjamin
Deposited On:08 Jan 2020 08:30
Last Modified:08 Jan 2020 08:30

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.