How many data points are required for one revolution? I have a question about an FFT. I have recorded the vibration of a gearbox with a sampling rate of 48 kHz. The speed of the input is 50 Hz, the speed of the output is 12.5 Hz. At a sampling rate of 48 kHz I have 48000 data points per second and after the output rotates 12.5 times per second I have (48000/12.5) 3840 points per rotation.

Does it make a difference if I use only 3840 points for the FFT (i.e. 1 revolution) or if I use 48000 points and thus have 12.5 revolutions?

If someone has literature on this or can explain to me whether there are rules for this, I would be very grateful.
@Hustler. Hi. I suggest you use all 48000 vibration samples. As a general rule: In mathematical analysis of measured data it's preferred that you use all available data to estimate some physical quantity. If you perform 48000-point DFTs (discrete Fourier transforms) your first DFT bin frequency will be zero Hz and your DFT bin spacing will be (as jithin said) 1 Hz. Your full spectrum analysis width will be zero –to- 24000 Hz.
When you see a narrow spectral peak in your DFT spectral magnitude samples you can relate the frequency (vibrations/second) of that spectral peak to some gear in your gear box by knowing the gear's rotational velocity and how many teeth the gear has.
If you are forced to use radix-2 FFT (fast Fourier transform) software, I suggest you zero-pad your 48000 samples out to a length of 65536 samples (2^16 samples) and then perform a 65536-point FFT. In that situation the FFT spectral bin spacing will be 48000/65536 = 0.7324 Hz and your full spectrum analysis width will again be zero –to- 24000 Hz.