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Fast implementation of frequency domain decomposition (FDD) in python with multiple identification techniques

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EFDD (Enhanced Frequency Domain Decomposition)

This module implements the Enhanced Frequency Domain Decomposition (EFDD) method for structural health monitoring using acceleration data.

Installation

pip install numpy scipy

Usage

import numpy as np
from efdd import EFDD

# Example data
Acc = # Acceleration data (samples x channels)
fs = 1000  # Sampling frequency
Nc = 4  # Number of channels

# Initialize EFDD
efdd = EFDD(Acc, fs, Nc)

# Calculate PSD matrix
psd, freq = efdd.get_psd_matrix()

# Get eigenvalues and mode shapes
single_value, mode_shapes = efdd.get_eigen_values()

# Calculate MAC value between two mode shapes
mac_value = efdd.MacVal(mode_shapes[:, 0], mode_shapes[:, 1])

# Get mode shape for a specific frequency
mode_shape = efdd.get_modeshape(frequency=10.0)

print("Mode Shape at 10 Hz:", mode_shape)

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Fast implementation of frequency domain decomposition (FDD) in python with multiple identification techniques

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