Experimental and operational modal analysis.
Project description
Experimental and operational modal analysis
Check out the documentation.
New in pyEMA 0.23
Normal modes computation
MAC and auto MAC matrix computation
- Extended pole picking functionallity
Cluster diagram
Selection of FRF display
Basic usage
Make an instance of Model class:
a = pyema.Model( frf_matrix, frequency_array, lower=50, upper=10000, pol_order_high=60 )
Compute poles:
a.get_poles()
Determine correct poles:
The stable poles can be determined in two ways:
Display stability chart (deprecated)
a.stab_chart()
or use the new function that also contains the stability chart and more:
a.select_poles()
The stability chart displayes calculated poles and the user can hand-pick the stable ones. Reconstruction is done on-the-fly. In this case the reconstruction is not necessary since the user can access FRF matrix and modal constant matrix:
a.H # FRF matrix a.A # modal constants matrix
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)
In this case, the reconstruction is not computed. get_constants must be called (see below).
Natural frequencies and damping coefficients can now be accessed:
a.nat_freq # natrual frequencies a.nat_xi # damping coefficients
Reconstruction:
There are two types of reconstruction possible:
Reconstruction using own poles:
H, A = a.get_constants(whose_poles='own', FRF_ind='all')
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, FRF_ind=‘all’)
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