TU Darmstadt / ULB / TUbiblio

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

Lorenzen, Steven Robert ; Riedel, Henrik ; Rupp, Maximilian Michael ; Schmeiser, Leon ; Berthold, Hagen ; Firus, Andrei ; Schneider, Jens (2022)
Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network.
In: Sensors, 2022, 22 (22)
doi: 10.26083/tuprints-00022977
Artikel, Zweitveröffentlichung, Verlagsversion

WarnungEs ist eine neuere Version dieses Eintrags verfügbar.

Kurzbeschreibung (Abstract)

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Lorenzen, Steven Robert ; Riedel, Henrik ; Rupp, Maximilian Michael ; Schmeiser, Leon ; Berthold, Hagen ; Firus, Andrei ; Schneider, Jens
Art des Eintrags: Zweitveröffentlichung
Titel: Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
Sprache: Englisch
Publikationsjahr: 2022
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 2022
Verlag: MDPI
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Sensors
Jahrgang/Volume einer Zeitschrift: 22
(Heft-)Nummer: 22
Kollation: 17 Seiten
DOI: 10.26083/tuprints-00022977
URL / URN: https://tuprints.ulb.tu-darmstadt.de/22977
Zugehörige Links:
Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (Abstract):

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

Freie Schlagworte: moving load localisation, nothing-on-road, free-of-axle-detector, bridge weigh-in-motion, structural health monitoring, field validation, continuous wavelet transformation, machine learning, fully convolutional networks
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-229771
Zusätzliche Informationen:

This article belongs to the Special Issue Advances in Condition Monitoring of Railway Infrastructures

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau
600 Technik, Medizin, angewandte Wissenschaften > 690 Hausbau, Bauhandwerk
Fachbereich(e)/-gebiet(e): 13 Fachbereich Bau- und Umweltingenieurwissenschaften
13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Statik und Konstruktion
Hinterlegungsdatum: 19 Dez 2022 12:32
Letzte Änderung: 20 Dez 2022 10:39
PPN:
Export:
Suche nach Titel in: TUfind oder in Google

Verfügbare Versionen dieses Eintrags

Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen