CF-5200 Frequently Asked Questions

The data length (the time T to capture 2048 sample points) changes depending on the frequency range. Therefore, changing the frequency range alters the time for FFT data, which results in a difference in energy.

To average and normalize energy, we consider the value of the power spectrum divided by T, that is, the power spectrum per unit time length of 1s (unit energy), and the power spectrum density is defined as the power spectrum per 1Hz.
In random signals, even the same signal can show differences in power spectrum values due to FFT resolution (corresponding to bandwidth). By normalizing and displaying the resolution (bandwidth) of the power spectrum per 1Hz, we can minimize the influence of differences due to frequency range.

The energy spectral density is defined as the power spectral density multiplied by the data length T.
In random signals, the power spectral density is used through averaging, but in single events such as shock waveforms, since the energy is finite in time, the energy spectral density that takes into account the duration of that phenomenon is utilized.

The relationships are as follows: data length (time) T and frequency resolution Δf.

• Power spectrum = Spectrum obtained from FFT with 2048 points
  (Displays the effective value of the amplitude for each frequency component)
• Power spectral density = Power spectrum ÷ Δf
  (Displays power spectrum per unit frequency)
• Energy spectral density = Power spectral density × T
  (Displays energy spectrum per unit frequency)

T = 2048 ÷ frequency range ÷ 2.56
Δf = frequency range ÷ 800

Chapter of the relevant instruction manual

5.8.3 Power Spectral Density / Energy Spectral Density