Overlap processing can only be achieved if the time required to calculate the FFT is shorter than the time record length. If this is not the case, the spectral calculations will lag behind the data acquisition leaving gaps of unanalyzed signal. See also the paragraph on real time speed later in this section.
If the overlap is 2/3, i.e., 66.7%, then the overall time weighting of the data will be flat, and there is no advantage to using a greater overlap. Most data collection for machinery analysis uses 50% data overlap, which provides adequate amplitude accuracy for most vibration work.
Here is a summary of the relationship between sampling rate, number of samples, time record length, and frequency resolution that affect FFT analysis. The sampling rate in samples per second, times the time record length T in seconds, equals the number of samples N. In the FFT analyzer, the number of samples N is constrained to a power of two.
The FFT algorithm, operating on N samples of time data produces N/2 frequency lines. Thus a time record of 512 samples will generate a spectrum of 256 lines. FFT analyzers generally do not display the upper spectral lines because of the possibility of their being contaminated by aliased components. This is because the anti-aliasing filter is not perfect, and has a finite slope in its cut-off range. Therefore, a 256 line spectrum will be displayed as a 200 line spectrum, and a 512-line spectrum will be displayed as a 400 line spectrum, etc.
The frequency resolution, DF, is equal to the frequency span divided by the number of lines, and this is equal to 1/T. Conversely, the time record length T equals 1/DF. From this it can be seen that as the frequency resolution increases (smaller DF), the time record length also increases in proportion. For this reason, to create a high-resolution spectrum requires a relatively long time to acquire the data.
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