Experimental and operational modal analysis.

## Project description

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)

## 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:

1. Display stability chart

a.select_poles()

The stability chart displayes calculated poles and the user can hand-pick the stable ones.

1. 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

### Reconstruction:

There are two types of reconstruction possible:

1. 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.

1. 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)

## Project details

This version 0.26 0.25 0.23 0.22 0.21 0.20 0.18 0.17 0.16 0.15 0.12 0.11 0.10

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