Bayesian operational modal analysis: Difference between revisions

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{{refimprovemore citations needed|date=December 2013}}

'''Bayesian operational modal analysis (BAYOMA)''' adopts a [[Bayesian inference|Bayesian]] [[system identification]] approach for [[operational modal analysis]] (OMA). Operational modal analysis aims at identifying the modal properties ([[natural frequency|natural frequencies]], [[damping ratio]]s, [[mode shape]]s, etc.) of a constructed structure using only its (output) vibration response (e.g., velocity, acceleration) measured under operating conditions. The (input) excitations to the structure are not measured but are assumed to be '[[Ambient vibrations|ambient]]' ('broadband random'). In a Bayesian context, the set of modal parameters are viewed as uncertain parameters or random variables whose probability distribution is updated from the prior distribution (before data) to the posterior distribution (after data). The peak(s) of the posterior distribution represents the most probable value(s) ('''MPV''') suggested by the data, while the spread of the distribution around the MPV reflects the remaining uncertainty of the parameters.

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|issue=2

|pages=81–95

|doi=10.1260/136943303769013183}}</ref> of ambient vibration data. Based on the formulation for FFT data, fast algorithms have been developed for computing the posterior statistics of modal parameters.<ref name="bayomabook" /> Recent developments based on [[EM algorithm]] <ref>

{{cite journal

|last=Li

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==Connection with [[maximum likelihood method]]==

Bayesian method and [[maximum likelihood method]] (non-Bayesian) are based on different philosophical perspectives but they are mathematically connected; see, e.g., <ref>

{{cite journal

|last=Au

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*Under a Gaussian approximation of the posterior distribution of parameters, their covariance matrix is equal to the inverse of Hessian of the negative log of likelihood function at the MPV. Generally, this covariance depends on data. However, if one assumes (hypothetically; non-Bayesian) that the data is indeed distributed as the likelihood function, then for large data size it can be shown that the covariance matrix is asymptotically equal to the inverse of the [[Fisher information]] matrix (FIM) of parameters (which has a non-Bayesian origin). This coincides with the [[Cramer–Rao bound]] in classical statistics, which gives the lower bound (in the sense of matrix inequality) of the ensemble variance of any unbiased estimator. Such lower bound can be reached by maximum-likelihood estimator for large data size.

*In the above context, for large data size the asymptotic covariance matrix of modal parameters depends on the 'true' parameter values (a non-Bayesian concept), often in an implicit manner. It turns out that by applying further assumptions such as small damping and high signal-to-noise ratio, the covariance matrix has mathematically manageable asymptotic form, which provides insights on the achievable precision limit of OMA and can be used to guide ambient vibration test planning. This is collectively referred as 'uncertainty law'.<ref name="ulaw2018"/>

==See also==

*[[Operational modal analysis]]

*[[Bayesian inference]]

*[[Ambient vibrations]]

*[[Microtremor]]

*[[Modal analysis]]

*[[Modal testing]]

==Notes==

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|publisher=Kluwer Academic Publisher

|___location=Boston

}}</ref> <ref>

{{cite book

|first=M. |last=Schipfors

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|publisher=Springer

|url=https://www.springer.com/gp/book/9781493907663}}

</ref> <ref>

{{cite book

|first=R. |last=Brincker

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|bibcode=2007MSSP...21.2359P

}}</ref>

==See also==

*[[Operational modal analysis]]

*[[Bayesian inference]]

*[[Ambient vibrations]]

*[[Microtremor]]

*[[Modal analysis]]

*[[Modal testing]]

==References==