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On the Use of Ensemble Empirical Mode Decomposition for the Identification of Bridge Frequency from the Responses Measured in a Passing Vehicle

Autor(en):
ORCID
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Infrastructures, , n. 2, v. 4
Seite(n): 32
DOI: 10.3390/infrastructures4020032
Abstrakt:

In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is created using the finite element (FE) method in Matlab. The EMD is employed to decompose the signals measured on the vehicle to their main components. It is shown that the bridge component of the response measured on the vehicle can be extracted using the EMD method. The influence of some factors, such as the road roughness profile and measurement noise, on the results are investigated. The results suggest that the EMD shows good performance under those conditions, but the accuracy of the results may still need to be improved. It is shown that in some cases, the EMD may not be able to decompose the signal effectively and includes mode mixing. This results in inaccuracies in the identification of bridge frequencies. The use of the ensemble empirical mode decomposition (EEMD) method is proposed to overcome the mode mixing problem. The influence of factors such as road profile, measurement noise and vehicle velocity are investigated. It is numerically demonstrated that employing the EEMD improves the results compared to the EMD.

Copyright: © 2019 the Authors. Licensee MDPI, Basel, Switzerland.
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Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10723286
  • Veröffentlicht am:
    22.04.2023
  • Geändert am:
    10.05.2023
 
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