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Time Domain Operational Modal Analysis
ARTeMIS Extractor and ARTeMIS Analyzer includes up to
four time domain modal analysis techniques. They are all of the data driven
Stochastic Subspace Identification (SSI) type and all implementing the powerful
Crystal Clear SSI feature. This feature result in extremely clear stabilization
diagrams with un-seen accuracy of the physical parameters and nearly no noise
modes.
Available
Techniques
The techniques available are: ·
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Unweighted Principal Component –
SSI-UPC
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Principal Component – SSI-PC
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Canonical Variate Analysis – SSI-CVA
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Unweighted Principal Component Merged Test Setups
These techniques estimate the modal
parameters directly from the raw measured time series. The SSI
techniques incorporate effective ways of dealing with noise. As a
result, the modal parameter estimations are the most accurate
commercially available today. The SSI techniques can work with
closely space and repeated modes with light or heavy damping. Since
they are working in time domain there are no leakage bias or lack of
frequency resolution. As a result, the modal parameter estimates are
asymptotically unbiased. Further, as the SSI techniques are low
model order estimators, the statistical errors of the modal
parameter estimates are extremely small. For more information, see
the general benefits of using SSI.
Stabilization
Diagrams
The SSI techniques estimate the parameters
of a range of models – so-called stochastic state space
realizations. From these estimated parameters the modal parameters
are extracted directly using a modal decomposition. By means
of the estimated natural frequencies the range of models are shown
in a stabilization diagram. By plotting the natural frequency
estimates of all models the physical modes reveal them selves as
straight vertical lines, whereas frequency estimates of noise modes
will be scattered all over the diagram.
Crystal Clear
SSI
The SSI techniques rely on linear least
squares estimation of the model using the raw measured time series.
In the past this estimation was unconditionally. Now the
revolutionary Crystal Clear SSI estimation feature allow a
conditional estimation emphasizing a user defined number of physical
modes and suppressing the remaining parts of the information in the
data. This results in extremely clear stabilization of the number of
modes specified and nearly no noise modes inside the visible
frequency range. For more information please see the detailed
description of the Crystal Clear SSI feature.
Automatic
versus Manual Modal Estimation
Ones the model parameters have been
estimated there are both automatic and manual ways to obtain the
modal parameters. In the manual approach, the model that gives the
best estimates of the modal parameters is selected from a
stabilization diagram. After this the physical modes are picked, by
using a set of modal indicators, and the modes can then be
visualized immediately. In the automatic approach, the modes are
found automatically by a search algorithm. This algorithm is
executed by a single click on a button.
Validation of Estimated Models
The performance of the estimated models can
be validated by displaying the synthesized spectra together with the
spectra of the measurements or by displaying the spectra of the
prediction errors between measurements and models.
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Stabilization diagram of the Unweighted Principal Component
technique using the Crystal Clear SSI estimation feature.
1. Select a range of models to estimate using drag and drop. 2.
Select how many poles (eigenvalues) to look for. 3. Leave the rest
to the Crystal Clear SSI estimator.
Validate models by comparing their performance with each other and
with the measurements.
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