One of the important functions of the FFT analyzer is that it is easily able to do averaging of spectra over time. In general, the vibration signal from a rotating machine is not completely deterministic, but has some random noise superimposed on it. Because the noise is unpredictable, it alters the spectrum shape, and in many cases can seriously distort the spectrum. If a series of spectra are averaged together, the noise will gradually assume a smooth shape, and the spectral peaks due to the deterministic part of the signal will stand out and their levels will be more accurately represented. It is not true that simply averaging FFT spectra will reduce the amount of the noise -- the noise will be smoothed but its level will not be reduced.
There are two types of averaging in general use in FFT analyzers, called linear averaging and exponential averaging. Linear averaging is the adding together of a number of spectra and then dividing the total by the number that was added. This is done for each line of the spectra and the result is a true arithmetic average on a line-by-line basis. Exponential averaging generates a continuous running average where the most recently collected spectra have more influence on the average than older ones. This provides a convenient form to examine changing data but still have the benefit of some averaging to smooth the spectra and reduce the apparent noisiness of them.
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