Two ways to see and understand vibration data

Time waveform analysis and FFT spectrum analysis Time waveform analysis

By mounting accelerometers at strategic points on bearings, we can measure acceleration and derive velocity. These velocity and acceleration measurements are recorded, analyzed and displayed as tables and plots by condition monitoring equipment. A plot of amplitude versus time is called a time waveform.

The featured time waveform plot illustrates how the signal from an accelerometer or velocity probe appears when graphed as amplitude over time. This type of vibration plot is also called a time domain plot or graph. Time waveforms display a short time sample of the raw vibration. Though typically not as useful as other analysis formats, time waveform analysis can provide clues to machine condition that are not always evident in the frequency spectrum and, when available, should be used as part of your analysis program.

FFT spectrum analysis

A method of viewing the vibration signal in a way that is more useful for analysis is to apply a Fast Fourier Transformation (FFT). In nonmathematical terms, this means that the signal is broken down into specific amplitudes at various component frequencies.

For example, we measure the signal’s amplitude at 10 Hz, then again at 20 Hz, etc., until we have a list of values for each frequency contained in the signal. These values or amplitudes are then plotted over the frequency scale. The number of component frequencies the waveform is divided into is referred to as the number of lines of resolution. The resulting plot is called an FFT spectrum. An FFT spectrum is an incredibly useful tool. If a machinery problem exists, FFT spectra provide information to help determine the location of the problem, the cause of the problem and, with trending, how long until the problem becomes critical. Because we know that certain machinery problems occur at certain frequencies, we analyze the FFT spectrum by looking for amplitude changes in certain frequency ranges.

To learn about other signal processing methods, check out the following post: “Getting a complete picture: the value of alternate signal processing methods”. 