2022-01-10T21:42:42.000-0500

Simcenter Testlab

Direct YouTube link: https://youtu.be/etrCZOY9Gtc

Overlap is a parameter used when determining the way time data is to be processed and converted into the frequency domain. It can have a big effect on the results, so understanding overlap is critical to a successful data processing session. This article will explain what overlap is, why it is useful, and show how it is applied in Simcenter Testlab software.

This article contains the following sections:

- What is overlap?
- Overlap definition
- Why: Window effects
- Why: Data insights

- Simcenter Testlab Stationary Averaging and Overlap
- What is stationary averaging?
- How is overlap specified?
- Examples

- Simcenter Testlab Tracked Averaging and Overlap
- What is tracked averaging?
- How is overlap specified?
- Examples

When processing data, be it from a microphone, accelerometer, or any other device, to view the frequency content of the signal we need to take a block of the time data and send it through the

As the name suggests, overlap occurs when two neighboring observation time blocks overlap each other and include the same time data. Overlap is designated as a percentage (%) and refers to the percentage of the observation time T that is overlapping the neighboring observation time block (see Figure 2).

The overlapping observations means the time data in the overlapped portion belongs to more than one FFT calculation. The overlapping observation times also result in less of the original time data being utilized for a fixed number of observations. For example, note in Figure 2a that two FFTs with 0% overlap uses a full 2T amount of time data, while in Figure 2c the same two FFTs overlapped 25% only uses 1.75T worth of time data.

So now that we know what overlap is, why is it useful? Two reasons will be covered in the next sections.

When investigating the frequency content of a measured signal, effectively using overlap can make the difference between getting the correct answer and completely missing important frequency content in your processed results (see Figure 3). The “dots” in the top plot of Figure 3 (0% Overlap) are caused by a window function used during the FFT calculation. Applying overlap can help overcome these window effects if used properly. The bottom plot of Figure 3 (90% Overlap) shows accurate amplitude levels for the

In order to reduce spectral

The Hanning window function gradually transitions from a value of zero at the beginning of the time block (0T) to a value of 1.0 at the midpoint (0.5T), and back to zero at the end (T). Multiplying the original measurement signal by this window function makes the signal periodic within the observation time (T) by forcing the function value to be zero (0) at the beginning and end of T. This results in a new, modified signal which differs from the original measurement (see Figure 5).

Supposing we want to make more than one FFT calculation over the course of our time data (as was done in Figure 3), we will need to consider the effect of the window functions used for each observation. If we process our time data with the observation times one after another with no overlap there will be portions of our original measurement that will not be used in any of the FFT calculations, as these portions are zeroed-out by the window (see Figure 6 below).

Consider a recording of a sine wave (Figure 6, top). If we choose to set the observation time to 1 second (T=1.0 second) and apply a Hanning window with 0% overlap (Figure 6, middle), we will end up with a 1-second FFT calculation for each second of the recording (Figure 6, bottom). The Hanning window has forced our measured signal to be periodic within our observation time, but in doing so has removed part of the original signal. These portions removed by the window cannot be recovered and will not be included in any of the FFT calculations.

In order to reduce the amount of data loss due to windowing, the observation times (and accompanying windows) can be overlapped. In the specific case of the Hanning window, the optimal minimum overlap is 50% - such that the maximum of the neighboring Hanning window is centered on the end of the previous observation block, where the Hanning window is at its minimum (see Figure 7). The 50% overlapped Hanning windows work together to ensure all parts of the original time signal will be processed, and nothing will be completely omitted. This effect is shown in Figure 8 below.

When processing data versus time or other tracking parameter, overlap can be used to improve the usefulness of processed results. Depending on how it is applied, overlap can help in two ways: either by providing additional detail on how a quantity is changing over time, or by providing a smoothing effect, making trends in the data more obvious (see Figure 9).

It has already been shown that increasing overlap results in more calculations per unit time. This effect can be used to gain additional granularity on how a quantity in the data is changing with respect to time by increasing the number of calculations in a given measurement length.

Conversely, in an instance where the data contains amplitude fluctuations which may be obfuscating an important trend, increased overlap can help to smooth these fluctuations, and make the trend easier to discern, as shown in Figure 9. These two overlap effects will be demonstrated on some examples in Section 3.

When multiple observation time blocks are processed for a given measurement (Figure 10a), one can specify how the FFT of these blocks are visualized in the frequency domain. In stationary averaging, the FFTs of observation time blocks #1 to #N are averaged together to produce a single frequency spectrum (Figure 2b). The other option is called tracked processing (Figure 10c) and will be discussed in section 3.

Overlap plays a vital role in stationary averaging and can make the difference between calculating the correct answer and completely missing important frequency content!

In Simcenter Testlab the type of processing (stationary vs tracked) is selected in the Acquisition parameters dialog of Throughput Processing. The selection is made in the Measurement mode dropdown as shown in Figure 11 below.

Once Stationary averaging is selected, the user can then select the Method. Options are Free Run, Time and Event. The Event method allows the user to calculate averages only when certain other criteria are met by the data (like a trigger function) and will not be discussed here.

The most straight-forward method to calculate an averaged spectrum with a particular amount of overlap is to choose the Free Run method, as shown in Figure 12.

With Free Run, the user specifies how many averages to take, and can specify the overlap percentage directly, regardless of any other parameters such as observation time (called “frame size” in Throughput processing). Averages will be computed until the number of requested averages have been calculated, or the end of the time history is reached. So, by setting the number of averages to a large value (i.e. – 5000) a user can ensure that averaging will continue at the specified overlap for the entirety of the measurement.

Obviously, the observation time (frame size) will also play a part in the number of averages calculated for a given time history, but the Free Run method automatically adjusts the number of averages per second to maintain the correct amount of overlap, whether the frame size is 1 second (1 Hz resolution) or 10 seconds (0.1 Hz resolution).

With the Time method, the situation is a bit reversed. Here, the user specifies the number of averages per second, regardless of frame size or overlap percentage (Figure 13).

So, for a given Acquisition rate (averages per second), the amount of overlap will be a function of the specified frame size (or frequency resolution). The frame size and corresponding frequency resolution are set on the “FS Acquisition“ tab of the Acquisition parameters dialogue as shown in Figure 14 (red box).

With the Time method in stationary averaging, the amount of overlap used in the averaging will be a function of the acquisition rate (averages per second) and the frame size (inverse of the frequency resolution).

As an example, consider the values specified in Figures 13 & 14. The acquisition rate is set to 2 averages per second (teal box, Fig 13). This means every 0.5 seconds we will initiate a new observation frame, or average, regardless of how long the observation frame is. If we consider the frame sizes shown in Figure 14, we see the channels in the Vibration group (blue boxes, Fig 14) have a frame size of 0.5 seconds (2 Hz resolution), while the channels in the Acoustic group (orange boxes Fig 14) have a frame size of 1.0 second (1 Hz resolution). So, every 0.5 seconds we are initiating an observation frame of 0.5 seconds for the channels in the Vibration group, and every 0.5 seconds initiating an observation frame of 1.0 second for the Acoustic group channels (see Figure 15).

This means that the averaged spectra for the Vibration group will be calculated with 0% overlap, while the averaged spectra for the Acoustic group will be calculated with 50% overlap. If a specific amount of overlap is desired, both the acquisition rate (averages per second) and the frame size must be taken into account when using the Time method to calculate an averaged frequency spectrum.

To illustrate the benefit of using overlap during stationary averaging, consider the signal shown in Figure 16. The measured signal is primarily a sinusoid, but with occasional high-frequency transient events. The end goal is to investigate the signal’s frequency content by calculating an averaged frequency spectrum using stationary averaging in Simcenter Testlab.

First, consider processing the measured signal with no overlap, and again using a 1-second observation time. This sets up the windowing scenario shown in Figure 17 below. Due to there being no overlap between the observation times, the Hanning windows completely remove the additional burst portion of the signal. As a result, this spectral content will be missing from the resulting frequency spectrum!

To minimize the impact of the Hanning windows on the processing of the original signal, an overlap of 50% is specified (Figure 18). With an observation time T = 1 second, 50% overlap means the second FFT calculation will begin half a second into the first observation time: odd-numbered observations begin on the integer values of time (0 s, 1 s, 2 s, etc) and the even-numbered observations begin on the half-second marks of the measurement (0.5 s, 1.5 s, 2.5 s, etc).

The odd-numbered observations will capture the sinusoid portion of the signal, and the even-numbered observations will be centered on the burst portion of the signal. The resulting reconstructed signal from all observations (even and odd numbered) will include nearly all the original signal content (Figure 19). Notice overlapping the observation times will also result in more calculations per unit time compared to no overlap.

The resulting effect of overlap can be seen in the averaged frequency spectra of both processing sets (Figure 20). By including an overlap of 50%, the high frequency component of the original signal is discovered, while in the 0% overlap case this high frequency component of the signal was removed by the Hanning window. As a result of applying a Hanning window without any overlap during our processing, the frequency content of the burst does not appear in the averaged frequency spectrum!

Instead of combining and averaging the results of calculations like in stationary averaging, the results In tracked processing are kept separate and plotted against time, rpm, or other tracking parameter. This technique makes it possible to see how the calculation result changes as a function of the tracking parameter. Tracked processing is often used to plot multiple FFTs versus time or rpm, and plotted in a waterfall plot, or colormap (Figure 21c). It is also possible to calculate single number metrics, such as overall level (OAL), psychoacoustic loudness, RMS, etc, versus the tracking parameter to investigate how the quantity changes as a function of the tracking parameter.

In Simcenter Testlab the type of processing (stationary vs tracked) is selected in the Acquisition parameters dialog of Throughput Processing. The selection is made in the Measurement mode dropdown as shown in Figure 22 below.

Once Tracked processing is selected, the user can then select the Tracking method. Options are Free Run, Tacho, Time, Static and Event. The Tacho method is discussed in detail in

As in Stationary averaging, the easiest way to perform tracked processing with a specific overlap percentage is to select the Free run mode, which allows the user to specify the overlap directly in the interface, as shown in Figure 23 below.

Here the user specifies the duration of the processing, rather than a number of averages like in Stationary averaging. To ensure the entire time history is processed, the Duration entered should be at least as long as the measurement. Enter the Overlap % and Testlab will automatically adjust the calculation increment based on the frame size to ensure the proper overlap.

Using the Time method in tracked processing is very similar to how it is used in stationary averaging. In tracked processing, the user specifies an increment, which is the amount of time between calculations. If this is set to 0.5 seconds as shown in Figure 24 below, an observation frame will be initiated every 0.5 seconds, regardless of how long the frame is. Similar to stationary averaging, the amount of overlap in tracked processing will be a function of both the increment and the frame size when using the Time tracking method.

When calculating a metric versus time for a measurement (i.e.- overall level, RMS, loudness, etc), overlap can be included in the processing, just as it is when performing stationary averaging. In tracked processing, there are two primary options or techniques to specify a particular amount of overlap in Simcenter Testlab: Free-run and Time. However, the two techniques have opposite effects on the resulting data. These effects are covered in the following examples.

Overall level was calculated with different amounts of overlap: 0%, 50%, and 90%. Increasing the amount of overlap results in more values being calculated, as shown in the legend of Figure 25. With no overlap (green) the overall level vs. time function has 31 data points. Overlapping at 50% (blue) results in 61 data points for the same record length. The amount of data points increases to 301 at 90% overlap (red). Notice the values of the three traces are identical when they intersect. Increasing the overlap allows for more insight as to how the overall level is fluctuating versus time.

Increasing the overlap averages more data points together for each increment step, which simplifies the trend and removes amplitude jitter. In this case, the increment was set to 0.1 seconds to preserve the detail of the changes in overall level versus time. The 0% overlap trace (green) features a frame of 0.1 seconds, and has amplitude values that vary widely from point to point. For 50% overlap (blue) the frame is increased to 0.2 seconds, or twice as many data points being averaged together for each calculation. For 90% overlap (red), a full second is required for each calculation which results in a much smoother transition between data points. Note that all the traces in Figure 26 have roughly the same number of data points (approximately 300).

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