So, I've been dealing with a series of pressure decay curves from a pressure transducer. Each one has a waterhammer effect as a consequence of shutting down pumps. Here are examples of 3 curves:
I'm using the scipy's
signal.irr to obtain the coefficients and
signal.filtfilt for filtering. Here are my parameters:
#filter parameters order = 1 (varied between 1 and 2) wn_log_log = 0.045 (This value has been adjusted to fit most of the curves, mostly arbitrary) rs = 0.05 rp=0.5 type = 'bessel' (between bessel and butterworth)
data points were sampled at 1 second.
I've had mixed results so far (in terms of the next calculation step).
Since I'm kind of unexperienced on signal processing I think my question will be how be able to intelligently choose a cut off frequency or adjust the filter parameters based on the nature of the signal which I assume is kind of similar.
But just a first pointer on an intelligent way of at least analyze this signal and have a better understanding of it (fourier analaysis, time domain-frequency domain methods, etc..) that would give me an idea of what would be a good cut-off frequency would be great.
So my goal is to be able to calculate the pressure difference from the filtered pressure and the I'll calculate the first and second derivative. My goal is to get three zones using the second derivative change of sign. The filter will need to be able to get a filtered pressure signal to identify these three zones. Some "wigles" will be ok, as long as zone 1 and 2 are clearly defined (sometime using the same filter to different curves doesn't allow me to achieve that)