Please use this identifier to cite or link to this item:
Title: Bayesian modal identification method based on general coherence model for asynchronous ambient data
Authors: Zhu, Yi-Chen
Au, Siu-Kui
Keywords: Engineering::Civil engineering
Issue Date: 2019
Source: Zhu, Y. & Au, S. (2019). Bayesian modal identification method based on general coherence model for asynchronous ambient data. Mechanical Systems and Signal Processing, 132, 194-210.
Journal: Mechanical Systems and Signal Processing
Abstract: A Bayesian frequency domain method for modal identification using asynchronous ambient data has been proposed previously. It provides a flexible and economical way to conduct ambient vibration tests as time synchronisation among data channels is not required. To simplify computation, zero coherence among synchronous data groups is assumed in the method, which inevitably introduces modelling error and lacks the ability of quantifying the synchronisation degree among different groups. To address these issues, a Bayesian modal identification method with a general coherence assumption among synchronisation groups is proposed in this paper. Computational difficulties are addressed and an efficient algorithm for determining the most probable values of modal properties is proposed. Synthetic and laboratory data examples are presented to validate the proposed method. It is also applied to modal identification of a full-scale ambient test, which illustrates the feasibility of the proposed method to real asynchronous data under field test configurations. For the cases investigated the proposed method does not lead to significant improvement in the identification accuracy of modal parameters compared to the method with zero coherence assumption. This is consistent with previous experience regarding the robustness of the zero coherence assumption and is now verified in this work. One may use the latter in practice for computational efficiency if the synchronisation degree among different groups is not demanded.
ISSN: 0888-3270
DOI: 10.1016/j.ymssp.2019.06.025
Schools: School of Civil and Environmental Engineering 
Rights: © 2019 Elsevier Ltd. All rights reserved. This paper was published in Mechanical Systems and Signal Processing and is made available with permission of Elsevier Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

Citations 50

Updated on May 23, 2024

Page view(s)

Updated on May 24, 2024

Download(s) 50

Updated on May 24, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.