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, 2019-10-28 - 2019-11-01, Tokyo, Japan.
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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/ | ||||||||||||||||||||
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Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||||||
Title: | Detecting singular track defects by time-frequency signal separation of axle-box acceleration data | ||||||||||||||||||||
Authors: |
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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 Start Date: | 28 October 2019 | ||||||||||||||||||||
Event End Date: | 1 November 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 (old) | ||||||||||||||||||||
Location: | Berlin-Adlershof | ||||||||||||||||||||
Institutes and Institutions: | Institute of Transportation Systems Institute of Transportation Systems > Data Management and Knowledge Discovery | ||||||||||||||||||||
Deposited By: | Baasch, Dr. Benjamin | ||||||||||||||||||||
Deposited On: | 08 Jan 2020 08:30 | ||||||||||||||||||||
Last Modified: | 24 Apr 2024 20:25 |
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