They are selected based on the highest peak acceleration level within the user-defined analysis range. In ObserVIEW, you can automatically find the orders with the highest amplitude. With order analysis, engineers can identify how the vibration of an individual component contributes to the overall level. Orders identify the relationship between the response of a rotational component at a specific amplitude, the RPM, and the frequency of rotation. Spectrograms can also be used to identify order lines. Still, atypical bands can indicate very useful information regarding potential damage. In a spectrogram, there are many indicators of damage and they can be complex. In comparison to an FFT, a spectrogram gives a better look into how the vibration changes over time. It illustrates the patterns of energy change which may not be visible in an FFT or PSD. For this reason, a spectrogram is a helpful tool for analyzing real-world data where there are various frequency components and/or mechanical and electrical noise.Ī spectrogram is most helpful for vibration analysis in a changing environment. As a collection of time-frequency analyses, the spectrogram can be used to identify characteristics of nonstationary or nonlinear signals. How Spectrograms Differ from other Signal Processing AnalysesĪ time-domain analysis can point out a defect in a DUT but does not specify the location or nature of the defect. The result is a jagged spectrogram with many gaps in the data. In the graphs below, the number of FFTs is reduced from 500 to 50. Conversely, a 1-minute spectrogram can be defined with 1000 FFTs, which would cover all time samples with some overlap between FFT analyses. However, there would be many gaps between FFT analyses. The Frame Count parameter determines the number of FFTs used to create the spectrogram and, as a result, the amount of the overall time signal that is split into independent FFTs.įor instance, it is possible to define a spectrogram covering 10 hours with only 10 FFT frames. The spectrogram is a plot of the spectrum on each segment.
Then, the fast Fourier transform (FFT) is applied to each segment. To generate a spectrogram, a time-domain signal is divided into shorter segments of equal length. With the data, users can locate strong signals and determine how frequencies change over time. The color scale is red-green-blue, where blue corresponds to low amplitudes, or “loudness,” and red corresponds to high amplitudes.įor vibration testing, spectrograms can be used to analyze the frequency content of a waveform to distinguish different types of vibration. In ObserVIEW, the tachometer- and time-based spectrogram graph can be viewed in two or three dimensions. Spectrograms can be two-dimensional graphs with a third variable represented by colors or three-dimensional graphs with a fourth color variable. A spectrogram displays the strength of a signal over time at a waveform’s various frequencies.
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