Spectral vs Waveform Data

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Spectral data is displayed in the frequency domain while waveform data is in the time domain. Spectral information is obtained by applying a Fourier transform to waveform data; this converts the data to show that amplitude and phase of the vibration at different frequencies.

Both spectral and waveform data have unique advantages when used in analysis. For simple analysis where a single frequency is present in the data, either form of data can be used as shown in Figure 1 below. It is relatively simple to extract the amplitude, phase and frequency from both data sets.

Spectral vs Waveform

Figure 1. Illustration of simple waveform (left) and the corresponding spectral data (right).

Unfortunately most data is not as simple to analyze as like in Figure 1. Usually noise is present in the data, and multiple frequencies may be present. This makes it difficult to extract useful information from the waveform, and spectral data can be better for analysis. An example of this is shown in Figure 2.

Spectral vs Waveform

Figure 2. A waveform (left) with noise and multiple frequencies present; the corresponding spectrum (right) makes it easier to analyze the data.

The Fourier transform is a powerful tool for creating spectral data. It decomposes the time domain information that contains multiple frequencies; however, any random or non-periodic impacts will be ignored with this method and they will not show up in the spectrum. The crest factor of a waveform can be calculated in the time domain and it helps determine if any impacting is occurring, and its severity. This demonstrates why it is important to view both spectral and waveform information when analyzing data.