4.Advanced User Guidebook
28
Using FFT function
The FFT (fast Fourier transform) math function mathematically converts a time-domain
waveform into its frequency components. It is very useful for analyzing the input signal on
Oscilloscope. You can match these frequencies with known system frequencies, such as
system clocks, oscillators, or power supplies.
FFT function in this oscilloscope transforms 8192 data points of the time-domain signal
into its frequency components mathematically (the record length should be 10K or above).
The final frequency contains 4096 points ranging from 0Hz to Nyquist frequency.
Taking the FFT operation for example, the operation steps are as follows:
1.
Press the
Math
button to display the math menu in the bottom.
2.
Select
FFT
in the bottom menu.
3.
In the right menu, select
Source
as
CH1
.
4.
In the right menu, select
Window
. In the left menu, turn the
M
knob to select the
proper window type.
5.
In the right menu, select
Format
. In the left menu, turn the
M
knob to select
amplitude unit (
V RMS
,
Decibels
) or phase unit (
Radian
,
Degrees
).
6.
Select
Hori (Hz)
in the right menu; select repeatedly to make the
symbol in front
of the horizontal position value (the upper one), turn the
M
knob to adjust the
horizontal position of FFT waveform; then select to make the
symbol in front of
the time base value below, turn the
M
knob to adjust the time base of FFT waveform.
7.
Select
Vertical
in the right menu; do the same operations as above to set the vertical
position and vertical division.
To select the FFT window
■ There are 6 FFT windows. Each one has trade-offs between frequency resolution and
magnitude accuracy. What you want to measure and your source signal characteristics
help you to determine which window to use. Use the following guidelines to select the
best window.
Type
Characteristics
Window
Hamming
Better solution for magnitude than Rectangle, and
good for frequency as well. It has slightly better
frequency resolution than Hanning.
Recommend to use for:
Sine, periodic and narrow band random noise.
Transients or bursts where the signal levels
before and after the event are significantly
different.
