I am using dataset found online for SHM which uses accelerometer sensor to detect changes in the structure. I know that I can transform the time-domain data into a frequency-domain and into a time-frequency domain (Ex.: Wavelet). So, I have a few questions:
- What features I can extract from each domain transform? I know that I could use Statistical as features for Time-domain analysis. But, I would like to know other features that can be used for each transfer.
- Is there another kind of time-frequency domain transform? I know that wavelet and STFT.
- In the dataset, there are multiple files where several files have different damage scenarios and this can be found below:
Folder May 28:
1-105 Damage scenario 1:
1-20: D1 21-43: D2 44-65: D3 66-85: D4 86-105: D5 106-128: undamaged 181-273: undamaged
Where each file has 15 acceleration sensor data. So, How can I obtain features and form a feature matrix to be used for pattern recognition and machine learning? I am would like to know what is the typical approach in order to form a feature matrix so that it can be used in the machine learning to observe the pattern.
link to the dataset: http://users.metropolia.fi/~kullj/JrkwXyZGkhF/wooden_bridge_time_histories/
Note: I converted the wav file into a txt file where I got the acceleration data and the sampling frequency