How to Identify Each Components from Audio Signal?
Well...
If your shaft is rotating at, say 1200 RPM or 20 Hz, then all the significant sound produced by that rotation should be at harmonics of 20Hz.
If the turbine has 3 perfect blades, however, then it will be in exactly the same configuration 3 times for every rotation, so all of the sound produced by the rotation should be confined to multiples of 60 Hz.
Energy at the other harmonics of 20 Hz -- 20, 40, 80, 100, etc. -- that is above the noise floor would generally result from differences between the blades.
This of course ignores noise from other sources that are also synchronized to the shaft, which can mess up the analysis.
I have some audio files recorded from wind turbines, and I'm trying to do anomaly detection. The general idea is if a blade has a fault (e.g. cracking), the sound of this blade will differ with other two blades, so we can basically find a way to extract each blade's sound signal and compare the similarity / distance between them, if one of this signals has a significant difference, we can say the turbine is going to fail.
I only have some faulty samples, labels are lacking.
However, there seems to be no one doing this kind of work, and I met lots of troubles while attempting.
I've tried using stft to convert the signal to power spectrum, and some spikes show. How to identify each blade from the raw data? (Some related work use AutoEncoders to detect anomaly from audio, but in this task we want to use some similarity-based method.)
Anyone has good idea? Have some related work / paper to recommend?