Bayesian operational modal analysis: Difference between revisions

Revision as of 12:18, 6 May 2014

Revision as of 11:50, 9 May 2014

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{{refimprove|date=December 2013}}

'''Bayesian Operational Modal Analysis''' (BAYOMA) adopts a [[Bayesian inference|Bayesian]] [[system identification]] approach for [[Operational Modal Analysis]] (OMA). [[Operational Modal Analysis]] (OMA) 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 disributiondistribution (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.

==Pros and Cons==

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==References==

{{Reflist}}

[[Category:Wave mechanics]]