Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149983
Title: An expectation-maximization algorithm for Bayesian operational modal analysis with multiple (possibly close) modes
Authors: Li, Binbin
Au, Siu-Kui
Keywords: Engineering::Civil engineering
Issue Date: 2019
Source: Li, B. & Au, S. (2019). An expectation-maximization algorithm for Bayesian operational modal analysis with multiple (possibly close) modes. Mechanical Systems and Signal Processing, 132, 490-511. https://dx.doi.org/10.1016/j.ymssp.2019.06.036
Journal: Mechanical Systems and Signal Processing
Abstract: The Bayesian FFT method has gained attention in operational modal analysis of civil engineering structures. Not only the most probable value (MPV) of modal parameters can be computed efficiently, but also the identification uncertainty can be rigorously quantified in terms of posterior covariance matrix. A recently developed fast algorithm for general multiple (possibly close) modes was found to work well in most cases, but convergence could be slow or even fail in challenging situations. The algorithm is also tedious to computer-code. Aiming at resolving these issues, an expectation-maximization (EM) algorithm is developed by viewing the modal response as a latent variable. The parameter-expansion EM and the parabolic-extrapolation EM are further adopted, allowing mode shape norm constraints to be incorporated and accelerating convergence, respectively. A robust implementation is provided based on the QR and Cholesky decompositions, so that the computation can be done efficiently and reliably. Empirical studies verify the performance of the proposed EM algorithm. It offers a more efficient and robust (in terms of convergence) alternative that can be especially useful when the existing algorithm has difficulty to converge. In addition, it opens a way to compute the MPV in the Bayesian FFT method for other unexplored cases, e.g., multi-mode multi-setup problem.
URI: https://hdl.handle.net/10356/149983
ISSN: 0888-3270
DOI: 10.1016/j.ymssp.2019.06.036
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

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