# Feature Extraction of FFT for One Class SVM

I'm looking for a good way of extracting features from the frequency domain of vibration data for a one-class support vector machine.

The image below shows an example from the dataset found in this link. The dominant peak found at around 960 Hz corresponds to the "main frequency" of the system. E.g rotation*number of gears.

Im keeping this question fairly open ended, because i know there are several methods for doing this and I'm mainly looking for your suggestions and experience.

Would it be possible to analyze this so I get some specific information regarding these peaks caused by the faulty gear tooth?

• The question is unclear. I didn't get what you're after. – Royi Jul 22 '18 at 22:22

• It's hard to know how to approach it without knowing your background. But it basically works like this: What PCA does is basically to project $X$ to a lower dimensional space ($XW$) such that the variance explained is maximized. In your case, I would suggest to use log-power features instead of the raw power. Check this video for an intuition of PCA youtube.com/watch?v=kw9R0nD69OU – Juan S. Castaño Nov 24 '17 at 17:33