The frequency content of what is being measured will drive the choice of sensor used for measurement, and the sample rate required. You don't need an exact value, but you should have a rough order of magnitude idea of what the highest frequency is (0.5 Hz, 1 Hz, 10 Hz, etc.). If you don't get this correct, and you select a sample rate that is too low, you will completely miss measuring minimum and maximum extreme values, compromising all subsequent data analysis. The sensor response time must also also be checked to insure it is sufficiently shorter than the period for the maximum frequency of the factor being measured.
The best way to measure the frequency content is to use data acquisition equipment along with a sensor with a very low response time, and measure the factor under a wide scope of conditions while sampling at no less than 10 kHz. Then perform a fast fourier transform (FFT) on the data collected, and then plot the frequency versus peak amplitude results. The fundamental frequencies should be apparent.
The Fast Fourier Transform (FFT) provides you with the frequency content of time domain signal(s) in the frequency domain. Any signal can be thought of as a sum of a series of simple sinusoids of different frequencies, amplitudes, and phases. Fourier analysis or spectrum analysis will deconstruct a signal into its individual sine wave components. The result is the vibration amplitude as a function of frequency. The highest frequency that can be analyzed from a signal is limited by the one half the sample rate of the signal (Nyquist frequency). The duration of the signal analyzed will limit the frequency resolution. For a sample rate of Fs in Hz, and a recording duration of T in seconds, The frequency resolution in Hz will be Fs / T, and the maximum frequency in Hz that can be analyzed will be Fs/2.
The FFT signal analysis can be performed using freely available Python libraries, or I can perform the analysis for you using a professional application from NI called DIAdem.
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