FFT spectrum tips

It is almost a given today to analyze vibration using data in frequency domain or as a spectrum. Engineers rely so much on the spectrum analysis that it is easy to forget that there are accuracy trade offs that are built into the process of obtaining the spectrum. Windowing is one of them. It is built in almost all vibration analyzers and

FFT is based on the Fourier theorem, which assumes that we deal with an infinitely long time function. Since we don't have infinite time, we just take a snapshot of time data and make the algorithm think that this piece of data repeats infinitely. Since repeating the time data sample causes abrupt changes at the ends of the sample, the process causes a truncation error that is also called spectral leakage. A standard technique to deal with this problem is to use a Window filter. A variety of functions can be used, which brings the values at the beginning and the end of a time data sample to zero or close to zero. This solves the spectral leakage problem, however all window functions reduce the amount of useful data used in the FFT calculation and distort the spectrum, often causing the peaks in a spectrum appear wider and lower. If a vibration analyzer has a choice of window functions it is important to compare spectra processed with the same window function all the time.

If the same analyzer is used for vibration measurement and for modal analysis, the window functions have to be different. For example, for vibration measurements Hann (also called Hanning) window is often used, while for modal analysis a rectangular window (no window function) may be preferred. It is important that the window functions are set correctly each time the analyzer is used for a new task.