Experimental and operational modal analysis.
Experimental and operational modal analysis
Check out the documentation.
New in version 0.26
include (or exclude) upper and lower residuals
driving point implementation (scaling modal constants to modal shapes)
implementation of the LSFD method that assumes proportional damping (modal constants are real-valued)
FRF type implementation (enables the use of accelerance, mobility or receptance)
Make an instance of Model class:
a = pyema.Model( frf_matrix, frequency_array, lower=50, upper=10000, pol_order_high=60 )
Determine correct poles:
The stable poles can be determined in two ways:
Display stability chart
The stability chart displayes calculated poles and the user can hand-pick the stable ones.
If the approximate values of natural frequencies are already known, it is not necessary to display the stability chart:
approx_nat_freq = [314, 864] a.select_closest_poles(approx_nat_freq)
After the stable poles are selected, the natural frequencies and damping coefficients can now be accessed:
a.nat_freq # natrual frequencies a.nat_xi # damping coefficients
There are two types of reconstruction possible:
Reconstruction using own poles (the default option):
H, A = a.get_constants(whose_poles='own')
where H is reconstructed FRF matrix and A is a matrix of modal constants.
Reconstruction on c using poles from a:
c = pyema.Model(frf_matrix, frequency_array, lower=50, upper=10000, pol_order_high=60) H, A = c.get_constants(whose_poles=a)
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