I have the bearing fault diagnosis experimental vibration data with acoustic for a healthy and different fault conditions with different speeds. I want to use the ML algorithm, but before that, I must do the feature extraction (statistical features) to train the ML or other things else?
Pandiyan Mu
Some steps you can follow for feature extraction:
1. Preprocessing: Clean and preprocess your data to remove any noise or artifacts that may interfere with the feature extraction process. This may involve filtering, normalization, or resampling the data.
2. Windowing: Split your data into smaller windows or segments. This is done to capture local characteristics and ensure that the statistical features are computed on meaningful subsets of the data. The window size will depend on the characteristics of your data and the expected fault frequencies.
3. Statistical Features: Compute various statistical features from each windowed segment. Some commonly used statistical features for vibration and acoustic data include:
- Mean: Average value of the data within each window.
- Standard Deviation: Measure of the dispersion of the data within each window.
- RMS (Root Mean Square): Square root of the average of the squared values within each window, providing information about the signal's energy.
- Kurtosis: Measure of the peakedness or flatness of the data distribution within each window.
- Skewness: Measure of the asymmetry of the data distribution within each window.
- Crest Factor: Ratio of the peak value to the RMS value within each window, indicating the signal's peakiness.
Additionally, you can consider other statistical features such as minimum, maximum, range, variance, and percentile values.
4. Feature Selection: After computing the statistical features, you may want to perform feature selection to identify the most relevant and informative features for your ML model. This can help reduce dimensionality and improve the model's performance. Techniques like correlation analysis, feature importance ranking, or domain knowledge can be used for feature selection.
Once you have extracted the relevant features, you can use them as input to train your ML algorithm.
Good luck: partial credit AI
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