How An "FFT" Plot Is Created ?

First, the vibration is "sampled" (collected) over a pre-determined period of time. The period of time used for the sample will be based on parameters programmed into either the database (for interval-based, route data collection) or the analyzer (for in-depth, or "spot", analysis).

Although sometimes a relatively simple sine wave, it will far more often be a complex signal with a number of different frequency components.  The "complex" signal shown below (still simplistic compared to data collected on most real machines) is made up of a 1x rpm component (e.g unbalance) and a 5x rpm component (e.g. number of vanes on the impeller - "vane pass" frequency) being generated by the machine. There can be (and usually are) far more influences - background (frictional) noise, misalignment, bearing problems, soft foot, looseness, frequency modulation, amplitude modulation, etc., etc., etc.

What the transducer actually 'senses' is an analog signal - one that mirrors the actual movement of the bearing at the location of the transducer. The signal processing that follows the analog signal collection consists of a couple of mathematical processes: A/D converter - Converts the analog signal to a digital one. Fourier Transform - This process is based on the principle that any periodic signal (e.g. vibration) can be broken down into a series of simple sinewaves that, when combined, result in the shape of the original signal.  Using the above "complex" signal as an example, in practical terms the principle means that the FFT process can deduce the two frequencies (1x and 5x) that were present to create the signal we see. The signal is fairly simple, though. Despite the presence of two signals, even we could make that judgement.

That principle, however, can be extended to any periodic signal. For each signal the FFT analyzes, there is one and only one mathematical solution to the problem - a specific series of simple sinewaves of precise amplitude values and phase relationships (which do NOT show up on the resulting plot but ARE considered by the FFT as we will see later) that, when combined, create the precise shape of the signal the FFT is analyzing.

The FFT process is an extremely complex mathematical process that is being applied to mechanical vibrations. Although a fairly reliable and useful tool, it MUST be understood that a spectrum is always suspect because these mathematical processes (A/D and FFT) often cause either or both of the following to happen: Vibration peaks get added (like sidebands and harmonics) that don't actually exist. That is not to say either are to be ignored - they can still provide valuable clues to the analyst. Occurrences (events) that may be obvious when viewing the raw time domain signal are completely lost. It is the signal shape that is being analyzed and deviations due to any mechanical problem from purely sinusoidal motion can cause the above phenomenon (harmonics, sidebands) to occur. For these reasons, it is strongly recommended that at the very least the time domain be used where it is most useful and the spectrum is the weakest: Slow Speed Equipment Gear Applications Sleeve (Plain) Bearings The reasons for this lie in what the FFT process actually does and what factors influence its output (the spectrum).