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What is a Spectrogram?

Analysis

A spectrogram displays the strength of a signal over time at a waveform’s various frequencies. Spectrograms can be two-dimensional graphs with a third variable represented by colors or three-dimensional graphs with a fourth color variable.

In ObserVIEW, the tachometer- and time-based spectrogram graph can be viewed in two or three dimensions. The color scale is red-green-blue, where blue corresponds to low amplitudes, or “loudness,” and red corresponds to high amplitudes.

 

For vibration testing, spectrograms can be used to analyze the frequency content of a waveform to distinguish different types of vibration. With the data, users can locate strong signals and determine how frequencies change over time.

Generating a Spectrogram

To generate a spectrogram, a time-domain signal is divided into shorter segments of equal length. Then, the fast Fourier transform (FFT) is applied to each segment. The spectrogram is a plot of the spectrum on each segment. 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.

spectrogram with horizontal cross section and vertical cursor

For instance, it is possible to define a spectrogram covering 10 hours with only 10 FFT frames. However, there would be many gaps between FFT analyses. Conversely, a 1-minute spectrogram can be defined with 1000 FFTs, which would cover all time samples with some overlap between FFT analyses.

In the graphs below, the number of FFTs is reduced from 500 to 50. The result is a jagged spectrogram with many gaps in the data.

500 frame count time spectrogram50 frame count time spectrogram

How Spectrograms Differ from other Signal Processing Analyses

A time-domain analysis can point out a defect in a DUT but does not specify the location or nature of the defect. As a collection of time-frequency analyses, the spectrogram can be used to identify characteristics of nonstationary or nonlinear signals. 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.

A spectrogram is most helpful for vibration analysis in a changing environment. It illustrates the patterns of energy change which may not be visible in an FFT or PSD. In comparison to an FFT, a spectrogram gives a better look into how the vibration changes over time.

In a spectrogram, there are many indicators of damage and they can be complex. Still, atypical bands can indicate very useful information regarding potential damage.

Order Analysis and Spectrograms

Spectrograms can also be used to identify order lines. Orders identify the relationship between the response of a rotational component at a specific amplitude, the RPM, and the frequency of rotation. With order analysis, engineers can identify how the vibration of an individual component contributes to the overall level.

In ObserVIEW, you can automatically find the orders with the highest amplitude. They are selected based on the highest peak acceleration level within the user-defined analysis range.

ObserVIEW Sine Tracking Analysis and Generation (STAG) screenshot

Spectrogram Analysis in ObserVIEW

In ObserVIEW, there is a time spectrogram and tachometer spectrogram graph option.

For the time spectrogram, the following settings can be adjusted:

  • Frame count: number of FFTs that make up the spectrogram
  • Min and max frequency: limits the frequency range of the spectrogram plot

The FFT spacing and FFT width are properties of the amount of time analyzed with respect to the FFT sample count and number of FFTs. The user cannot define these properties directly.

  • FFT spacing: distance in time between FFT anchor points (left, center, right); an approximation based on the spectrogram range and frame count
  • FFT width: width of time data each FFT represents

In addition to these settings, the tach spectrogram includes:

tachometer-based spectrogram

  • Min and max tach values: limit the tachometer range of the spectrogram. For example, these values may be changed to focus on a subset of the total RPM range or to purposely exclude errant data.
  • Tach sweep direction: triggers spectrogram readings and can be configured to match the direction of the tach trace sweep. If the tach sweep direction doesn’t match the direction of the tach sweep, the tool will report little to no readings.

There are several graph options that are useful for analysis. These options can be found under Spectrogram Graph Options.

  • The mouse cursor displays the X, Y, and Z values of the data under the cursor
  • The horizontal cross-section displays a graph under the spectrogram with the data for a horizontal slice across the spectrogram. The slice can be moved by dragging the horizontal crossbar up and down on the spectrogram.
  • The vertical cross-section displays a graph to the right of the spectrogram with the data for a vertical slice down the spectrogram. Again, the slice can be moved by dragging the vertical crossbar left and right across the spectrogram. The vertical cross-section can be rotated.

Both the horizontal and vertical cross-sections can be used to examine a slice of all data at a specific time or frequency.

In addition to these options, the tach spectrogram includes the option to enable an order cross-section graph.

Conclusion

The spectrogram is another tool for device maintenance and detection of error. To learn more, visit our ObserVIEW analysis software page or download a free demo of the software.

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Date

May 14, 2020

Author

Jade Vande Kamp

Category

Analysis

References

  • Griffaton, Julien, José Picheral, and Arthur Tenenhaus. 2014. “Enhanced visual analysis of aircraft engines based on spectrograms.” Paper presented at ISMA2014, Leuven, Belgium, September 2014, 2809-2822. HAL: hal-01103775.
  • “What Is A Spectrogram?” Pacific Northwest Seismic Network. URL: pnsn.org.
  • Yan, Ruqiang and Robert X. Gao. “Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis.” Tribology International 42, no. 2 (February 2009): 293-302. DOI: 10.1016/j.triboint.2008.06.013.

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