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Major Benefits of
using SSI in General |
There are some
technical benefits of the time domain Stochastic Subspace
Identification SSI
algorithms compared to other commercially available
parametric model estimators working in the frequency domain and
relying on the estimation of half-power spectral densities.
These benefits are the
same no matter if the
traditional SSI
algorithm or the
revolutionary new
Crystal Clear SSI
algorithm are used, and with or without the use of
Automatic Mode Estimation for SSI.
Unbiased estimation – No systematic estimation errors
No leakage – The SSI algorithms work in time domain and
are
data-driven methods. Since the model estimation is not relying on any
Fourier transformations to frequency domain no leakage is introduced.
Leakage is always introduced when applying the Fourier transformation
and assuming periodicity. Leakage always results in an unpredictable overestimation
of the damping.
No
problems with deterministic signals (harmonics) – Since the modal
parameters are extracted directly by fitting parameters to the raw
measured time histories, the presence of deterministic signals, such
as harmonics introduced by rotating machinery, does not create problems. Harmonics are just estimated as very lightly damped modes.
Methods relying on the estimation of half power spectral densities all
assume that the excitation is broad-banded (white noise), and the presence of
deterministic signals introduce bias in the modal parameter estimation.
Less
random errors
Low-order model estimator - SSI algorithms are
born linear least-squares fitting techniques fitting state space
systems with correct noise modeling. This leads to the use of much
smaller model orders than other commercially available high order
model estimators. These estimators are often used to approximate a non-linear least squares problem
with
a linear least-squares fitting problem. This is an often seen approximation when fitting e.g. polynomial matrix fractions. In order for this
approximation to work, a high-order polynomial order is needed. Since
this leads to the use of many parameters compared to a low-order
technique, the uncertainties of the high-order parameter estimates
becomes larger. More parameters are fitted with the same amount of
data available, meaning less independent information per estimated
parameter.
All
modal parameters are fitted in one operation. All parameters fitted
are taking advantage of the noise cancellation techniques of the
orthogonal projection of SSI. Other commercially available methods
often fit the poles (frequency and damping) first, and then use the
noisy spectral data and the estimated poles to fit the mode shapes resulting in
poor mode shape estimates.
Crystal Clear SSI analysis. SSI algorithms are born unbiased and use low model orders
resulting in small random errors of the modal parameter estimates.
Try it today by downloading the
30 days evaluation version of ARTeMIS Extractor Pro. Just install it and run one
of the Example Files using the Preliminary Modal Analysis feature. This will
demonstrate the efficiency and ease-of-use of Crystal Clear SSI and Automatic
Mode Estimation for SSI in a matter of minutes.
Download here |
