Simcenter Testing Solutions What is a Power Spectral Density (PSD)?

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What is a Power Spectral Density (PSD)? How is it different than an autopower?

A Power Spectral Density (PSD) is the measure of signal's power content versus frequency. A PSD is typically used to characterize broadband random signals. The amplitude of the PSD is normalized by the spectral resolution employed to digitize the signal.

For vibration data, a PSD has amplitude units of g2/Hz. While this unit may not seem intuitive at first, it helps ensure that random data can be overlaid and compared independently of the spectral resolution used to measure the data. This article explains how this is done.

To understand a Power Spectral Density (PSD), it is helpful to understand some limitations of an autopower function when analyzing data with differing spectral resolutions:

Scenario:

Identical broad band data was measured three different times. For each measurement, only the frequency resolution was changed. It was acquired with a 1 Hz frequency resolution, then a 4 Hz frequency resolution, and finally an 8 Hz frequency resolution.

The resulting autopower data has very different amplitudes (Picture 1). Why? Which one is correct?

Picture 1: Autopowers of identical broadband data measured with 1 Hz spectral resolution (red), 4 Hz spectral resolution (green) and 8 Hz spectral resolution (blue)

Answer:

All of the autopowers are correct! They appear visually to have different amplitudes however, which is confusing.

As the frequency resolution (sometimes referred to as spectral resolution) gets finer (starting with 8 Hz, then 4 Hz, then 1 Hz), more data points are being used to measure the signal. The same signal is being divided up into smaller parts, but the total remains the same.

While the amplitude at individual frequencies appears to be different, the total summation of data across the frequency range is identical.

Picture 2: Overall RMS summation value displayed in legend (upper right) is identical for all three autopowers

The “total” of the signal is reflected in the RMS summation of the entire spectrum as shown in Picture 2. The RMS summation is identical for all three autopower spectrums.

Picture 3: Partial RMS value for 2000 to 4000 Hz is identical for all three autopowers. Partial RMS displayed on left.
 

Even a partial RMS summation of the data (Picture 3), based on a smaller frequency range, is identical for all three measurements.

Spectral Lines

Spectral lines are the key to understanding why the plotted amplitudes for these identical signals looks to different, but sum to the same amount.

An autopower spectrum, measured with a 1 Hz resolution and a bandwidth of 6000 Hz, would have 6000 data points or spectral lines as shown in Equation 1.

Equation 1: The number of spectral lines is determined by dividing the bandwidth by the frequency resolution.

Spectral lines are discrete points in the frequency domain used to digitize the spectrum (remember that a computer cannot store a continuous analog function, it must break any data into discrete points). The entire signal is divided up among these 6000 data points (i.e., spectral lines).

For the three separate measurements (each with a max frequency of 6000 Hz), the following is observed:

• 1 Hz frequency resolution -> 6000 spectral lines -> lowest apparent amplitude
• 4 Hz frequency resolution -> 1500 spectral lines -> mid-range apparent amplitude
• 8 Hz frequency resolution -> 750 spectral lines -> highest apparent amplitude

Note that the amplitude is really a function of the number of spectral lines. The more spectral lines, the lower the amplitude of each spectral line.

The results are correct for a given frequency resolution, although this is not readily apparent when viewing the spectrum. By zooming into a narrower frequency range (Picture 4), the different frequency resolutions between the three measurements start to become more obvious. One can see that the blue line (8 Hz resolution) has less data points than the red line (1 Hz resolution).

Picture 4: Zoomed in frequency range of 2000 to 2300 for three autopowers (red=1 Hz, green=4 Hz, blue=8 Hz frequency resolution)

The manner in which the data is presented disguises the differences between the data curves. By default, most FFT analyzers display data with lines connecting the data points. Instead of connecting with the data with lines as in Picture 4, the same data can instead be viewed as block outlines (Picture 5). Block outlines allow the individual spectral lines to be seen.

Picture 5: Data block presentation of frequency range of 2000 to 2300 for three autopowers (red=1 Hz, green=4 Hz, blue=8 Hz frequency resolution).
 

Using the block outlines, the differences in the three measurements is more obvious (Picture 5). One can see that in the blue curve, measured with 8 Hz frequency resolution, that the levels of each spectral line are higher, but there are fewer data points over the frequency range. In the red curve, there are more data points, but each point/line is lower in amplitude.The green curve is in the middle.

Party Analogy

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A party where beverages are being served can be used as an analogy (Picture 6) to explain this relationship between frequency resolution and amplitude in the autopower.

Imagine that the signal being measured is a fixed quantity of beverage to be served. The number of glasses held by attendees is analogous to the number of spectral lines.

Picture 6: Party analogy for amplitude versus frequency resolution of broadband random signal. The amplitude levels in each glass decrease as the number of glasses increase
 

To simulate a broadband signal, the beverage is distributed evenly among the glasses. The more glasses (i.e., spectral lines) for distribution, the lower the amount in each glass. The total amount of beverage (i.e., RMS) served remains the same.

The Power Spectral Density function will now be used to remove/reduce the apparent difference in the three autopower spectrums. Remember, the Autopower and Power Spectral Density are both correct, only the representation of the data is being changed by switching functions.

Power Spectral Density

Despite the fact that the total amount of signal (as shown by the RMS) is identical, it is often desired that the amplitudes shown in the autopower graph also look similar.

Power Spectral Density (PSD) normalizes the amplitudes by the frequency resolution to give the amplitudes a similar appearance (Picture 7).

Picture 7: Power Spectral Density (PSDs) of identical broadband data measured with 1 Hz spectral resolution (red), 4 Hz spectral resolution (green) and 8 Hz spectral resolution (blue)
 

The term “normalizing” by the frequency resolution means dividing the amplitude of each spectral line by the frequency resolution.

• In the case of a 1 Hz frequency resolution, the amplitude would remain unchanged.
• For a 4 Hz frequency resolution, the amplitude is divided by 4 at each frequency.
• For an 8 Hz resolution, the amplitude is divided by 8 at each frequency.

The amplitude is always shown divided by Hertz as result, as in 25 g²/Hz.

Because a PSD gives similar amplitudes for random data, it is often used as the control function in random vibration control testing.

By convention, the amplitude of the data in a Power Spectral Density is squared. For example, if one were measuring a 5 g amplitude (rms) sine wave, the amplitude shown in a PSD would be 25 g²/Hz.

Sinusoidal Data

Everything is the opposite for sinusoidal data!

Going back to our party analogy, in the case of a sinusoid, all the signal in the pitcher is put into a single glass (spectral line). For example, a 200 Hz sine wave acquired with a 1 Hz, 4 Hz, or 8 Hz frequency resolution puts all the signal in a single spectral line (200 is evenly divisible by 1, 4, and 8).

Picture 8: Party analogy for amplitude versus frequency resolution of sinusoidal signal. The amplitude levels in an individual glass do not change with more glasses (i.e., spectral lines)
 

For a sinusoid, the amplitude will not vary greatly with changes in the number of spectral lines. Because the signal is always placed in one “glass” (ie, spectral line), the amplitude in the glass does not change as more glasses are added.

Picture 9: Autopowers of a 200 Hz sine wave measured with an 8 Hz frequency resolution (red), 4 Hz frequency resolution (green), and 1 Hz frequency resolution (blue). RMS values are identical as shown in legend in upper right.

In fact, a Power Spectral Density (PSD) of a sinusoidal signal would actually change the apparent amplitude of a sine wave drastically as in Picture 10.

Picture 10: Power Spectral Density functions of a 200 Hz sine wave measured with an 8 Hz frequency resolution (red), 4 Hz frequency resolution (green), and 1 Hz frequency resolution (blue). RMS values are identical as shown in legend in upper right.

This is because the amplitudes of the sine waves are being divided by their respective frequency resolutions (delta f). Taking the same amplitude and dividing it by different frequency resolution (delta f) values makes the amplitudes different.

The PSD in which we divided the amplitude value by largest frequency resolution (in this case 8 Hz), results in the lowest amplitude.

The RMS summation of the PSD of the sine waves remains the same. This is because the RMS summation functionality adjusts for the frequency resolution division automatically to get the correct value.

Conclusion

Because computers digitize signals into discrete points, some interesting digital signal processing phenomenon are created.

Broadband (random) signals:

• Autopower spectra with different frequency resolutions will have different amplitude levels.
• Power Spectral Densities with different frequency resolutions will have the same amplitude levels.

Sinusoidal (periodic) signals:

• Autopower spectra with different frequency resolutions will have the same amplitude levels.
• Power Spectral Densities with different frequency resolutions will have different amplitude levels.

Table 1 summarizes these observations about Autopowers versus Power Spectral Densities.

Table 1: Summary of Autopower versus Autopower PSD

In all cases, the RMS summation of data over the frequency range is the same in all cases. When evaluating a spectral function, it is best practice to uses a RMS for comparison purposes, since the RMS amplitudes take into account frequency resolution and other adjustments to produce consistent and useable values.

In practice, typically one finds in industry:

• Power Spectral Density – Used to quantify random vibration fatigue
• Autopower – Used to quantify sinusoidal data, for example, harmonics generated by engines, pumps, gears, etc.

Simcenter Testlab Format Conversion

In Simcenter Testlab (formerly LMS Test.Lab), an autopower can be converted to a PSD and vice versa.

In the conditioning toolbar (Picture 11), the format of a spectral function can be converted by selecting the "FFT Format Conversion" button.

Picture 11: “FFT Format Conversion” button in Navigator worksheet to convert to a PSD

In the menu, the following can be changed:

• 'Amplitude Scaling' - Select the amplitude mode between RMS and Peak.
• 'Spectrum Format' - Select between Linear, Power, PSD and ESD

While the mode and format can be changed, the spectral resolution cannot. To change the spectral resolution, the data must be reprocessed from the original time history.

Questions? Contact peter.schaldenbrand@siemens.com

Related Digital Data Acquisition Links:

• Index of Testing Knowledge Articles
• Gain, Range, Quantization
• Anti-Aliasing Filter
• Overloads
• Averaging Types: What's the difference?
• Spectrum versus Autopower
• Autopower Function...Demystified!
• Overlap: What, Why and How to Use It
• Shock Response Spectrum (SRS)
• Fatigue Damage Spectrum
• Windows and Leakage
• Window Types
• Window correction factors
• RMS Calculations
• The Gibbs Phenomenon
• Introduction to Filters: FIR and IIR
• Single Ended versus Differential Inputs
• AC versus DC Coupling
• What is a SN-Curve?
• Kurtosis
• NVH Testing and Data Acquisition
• Digital Data Acquisition and Signal Processing Seminar
• Mission Synthesis Seminar
• Digital Signal Processing YouTube List