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?
Assuming you have epochs/segments of data. For this kind of signals, it is a safe approach to extract features using wavelet representations.

Using FFT might work as well, but I dont know how problematic would be the stationarity assumption in this kind of applications. Besides, FFT estimates for this kind of signals is sometimes very noisy. If you insist on this kind of representation, I would suggest estimating the power spectral density using Welch or multitaper methods. These usually deliver a nicer spectral estimation.

In any case, using any spectral features as direct input to your SVM might not be the best idea. Irrespectively of what features you extract (FFT, Wavelets, etc..) I would include a data dimensionality reduction step (PCA would be the first thing I'd try)