Bayesian operational modal analysis: Difference between revisions

Revision as of 13:06, 26 December 2013

Revision as of 00:21, 1 January 2014

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{{ref improverefimprove|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|damping ratios]]s, [[mode shape|mode shapes]]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 disribution (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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The advantage of a [[Bayesian inference|Bayesian]] approach for OMA is that it provides a fundamental means via the Bayes' Theorem to process the information in the data for making statistical inference on the modal properties in a manner consistent with modeling assumptions and probability logic.

The potential disadvantage of Bayesian approach is that the theoretical formulation can be more involved and less intuitive than their non-Bayesian counterparts. Algorithms are needed for efficient computation of the statistics (e.g., mean and variance) of the modal parameters from the [[posterior distribution]].

==Methods==

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|volume=16

|issue=3

|pages=219-231219–231

|doi=10.1016/S0266-8920(01)00004-2}}</ref> and in the [[frequency ___domain]] using the [[spectral density]] matrix<ref>

{{cite journal

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|volume=30

|issue=

|pages=1103-11231103–1123

|doi=10.1002/eqe.53}}</ref> and [[FFT]] ([[Fast Fourier Transform]])<ref>

{{cite journal

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|volume=6

|issue=2

|pages=81-9581–95

|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>

{{cite journal

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|volume=126

|issue=

|pages=3-143–14

|doi=10.1016/j.compstruc.2012.12.015

|url=}}</ref> The fundamental precision limit of OMA has been investigated and presented as a set of '''uncertainty laws'''.<ref>

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|pages=

|doi=10.1016/j.ymssp.2013.07.017

|url=}}</ref>

==Notes==

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|volume=21

|issue=

|pages=2359-23732359–2373

|doi=10.1016/j.ymssp.2006.11.007

|url=}}</ref>

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*[[Modal analysis]]

*[[Modal testing]]

==References==

{{Reflist}}

{{Uncategorized|date=January 2014}}