https://github.com/alejoduarte23/fast_fdd

Fast implementation of frequency domain decomposition (FDD) in python with multiple identification techniques
https://github.com/alejoduarte23/fast_fdd

numpy scipy-signal

Last synced: about 2 months ago
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Fast implementation of frequency domain decomposition (FDD) in python with multiple identification techniques

• Host: GitHub
• URL: https://github.com/alejoduarte23/fast_fdd
• Owner: AlejoDuarte23
• Created: 2024-07-30T04:37:59.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-30T04:46:19.000Z (5 months ago)
• Last Synced: 2024-07-30T08:24:04.862Z (5 months ago)
• Topics: numpy, scipy-signal
• Language: Python
• Homepage:
• Size: 1000 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# EFDD (Enhanced Frequency Domain Decomposition)

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

## Installation

```bash

pip install numpy scipy

```

## Usage 

```python

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)

```