.png){kind=icon}
.png){kind=icon}
Return
Bayesian modal identification method based on general coherence model for asynchronous ambient data
.png){kind=icon}
OA
AI
DOI：10.1016/j.ymssp.2019.06.025.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Ambient data
Asynchronous data
Bayesian methods
General coherence model
Operational modal analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
1.3W
Citations:
6.6W