Bayesian operational modal analysis with asynchronous data, Part II: Posterior uncertainty

Yi Chen Zhu; Siu Kui Au

doi:10.1016/j.ymssp.2017.05.023

Bayesian operational modal analysis with asynchronous data, Part II: Posterior uncertainty

Yi Chen Zhu^*, Siu Kui Au

^*Corresponding author for this work

University of Liverpool

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

Abstract

A Bayesian modal identification method has been proposed in the companion paper that allows the most probable values of modal parameters to be determined using asynchronous ambient vibration data. This paper investigates the identification uncertainty of modal parameters in terms of their posterior covariance matrix. Computational issues are addressed. Analytical expressions are derived to allow the posterior covariance matrix to be evaluated accurately and efficiently. Synthetic, laboratory and field data examples are presented to verify the consistency, investigate potential modelling error and demonstrate practical applications.

Original language	English
Pages (from-to)	920-935
Number of pages	16
Journal	Mechanical Systems and Signal Processing
Volume	98
DOIs	https://doi.org/10.1016/j.ymssp.2017.05.023
Publication status	Published - Jan 1 2018
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Ltd

ASJC Scopus Subject Areas

Control and Systems Engineering
Signal Processing
Civil and Structural Engineering
Aerospace Engineering
Mechanical Engineering
Computer Science Applications

Keywords

Asynchronous data
Bayesian methods
FFT
Field test
Operational modal analysis
Posterior uncertainty

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10.1016/j.ymssp.2017.05.023

Cite this

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title = "Bayesian operational modal analysis with asynchronous data, Part II: Posterior uncertainty",

abstract = "A Bayesian modal identification method has been proposed in the companion paper that allows the most probable values of modal parameters to be determined using asynchronous ambient vibration data. This paper investigates the identification uncertainty of modal parameters in terms of their posterior covariance matrix. Computational issues are addressed. Analytical expressions are derived to allow the posterior covariance matrix to be evaluated accurately and efficiently. Synthetic, laboratory and field data examples are presented to verify the consistency, investigate potential modelling error and demonstrate practical applications.",

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N2 - A Bayesian modal identification method has been proposed in the companion paper that allows the most probable values of modal parameters to be determined using asynchronous ambient vibration data. This paper investigates the identification uncertainty of modal parameters in terms of their posterior covariance matrix. Computational issues are addressed. Analytical expressions are derived to allow the posterior covariance matrix to be evaluated accurately and efficiently. Synthetic, laboratory and field data examples are presented to verify the consistency, investigate potential modelling error and demonstrate practical applications.

AB - A Bayesian modal identification method has been proposed in the companion paper that allows the most probable values of modal parameters to be determined using asynchronous ambient vibration data. This paper investigates the identification uncertainty of modal parameters in terms of their posterior covariance matrix. Computational issues are addressed. Analytical expressions are derived to allow the posterior covariance matrix to be evaluated accurately and efficiently. Synthetic, laboratory and field data examples are presented to verify the consistency, investigate potential modelling error and demonstrate practical applications.

KW - Asynchronous data

KW - Bayesian methods

KW - FFT

KW - Field test

KW - Operational modal analysis

KW - Posterior uncertainty

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JO - Mechanical Systems and Signal Processing

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ER -

Bayesian operational modal analysis with asynchronous data, Part II: Posterior uncertainty

Abstract

Bibliographical note

ASJC Scopus Subject Areas

Keywords

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