pyEMA

Experimental and operational modal analysis

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)

a = pyema.Model(
    frf_matrix,
    frequency_array,
    lower=50,
    upper=10000,
    pol_order_high=60
    )

a.get_poles()

The stable poles can be determined in two ways:

a.select_poles()

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:

H, A = a.get_constants(whose_poles='own')

where H is reconstructed FRF matrix and A is a matrix of modal constants.

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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