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, 22 (22)
doi: 10.3390/s22228963
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
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: | Bibliographie |
| Titel: | Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Sensors |
| Jahrgang/Volume einer Zeitschrift: | 22 |
| (Heft-)Nummer: | 22 |
| Kollation: | 17 Seiten |
| DOI: | 10.3390/s22228963 |
| URL / URN: | https://www.mdpi.com/1424-8220/22/22/8963 |
| Zugehörige Links: | |
| 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 |
| Zusätzliche Informationen: | Artikel-ID: 8963 // 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 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Statik und Konstruktion > Fachgebiet Statik und Dynamik der Tragstrukturen (2024 umbenannt in "Fachgebiet datengetriebene Baudynamik") |
| Hinterlegungsdatum: | 08 Aug 2023 13:41 |
| Letzte Änderung: | 03 Jul 2024 02:59 |
| PPN: | 502263180 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network. (deposited 19 Dez 2022 12:32)
- Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network. (deposited 08 Aug 2023 13:41) [Gegenwärtig angezeigt]
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