 |
Figure 1: Applying the Trend operator to the
same input waveform illustrates how the
Trend is asynchronous to the input waveform. |
In a
previous post, we described the characteristics of the Track math function and two key applications of using Tracks to graph oscilloscope measurement data: anomaly detection and waveform demodulation. In this post, we'll discuss the characteristics and uses of the Trend function.
To illustrate an important distinction between Tracks and Trends, the Trend math operator in Figure 1 is now applied to the same signal as was the Track in our previous post without first reacquiring the input waveform.
Note that unlike a Track, the Trend is not time-synchronized to the input waveform. Only the order of events, and not the timing of events, is retained. The underlying shape of the Track may be displayed in the Trend because the same measurement values from a single acquisition are displayed in the same sequence—however, the timing information of when each of the values has occurred is not retained in the Trend. Therefore, unlike the Track, the Trend does not point to the location of an anomaly. Without time scaling, the Trend does not have the frequency information needed to demodulate an input waveform.
Use Trends to Observe Long-term Changes
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Figure 2: The Trend acts as a data logger
when acquiring single pulses. |
Unlike the Track, the Trend can retain measurement results from previous acquisitions, which helps an engineer to retain a history of previous measurements compared with new measurements. The Trend allows for an engineer to observe long term changes due to timing drift across multiple acquisitions, which would not be visible in a Track. For example, when heating or cooling the device under test in a thermal chamber to test environmental effects, the Trend can show long-term variation in measurement values as the device temperature changes in the thermal chamber.
Use Trends for Data Logging
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Figure 3: The most recent pulse width in the Trend
was acquired seven seconds after the previous one. |
Trends are ideal for data logging, especially when characterizing slowly changing phenomena. Figure 2 (above) shows only a single pulse within the waveform record, but there are many values in the Trend, because multiple waveforms are acquired, although only a single pulse is captured each time. The acquired pulse in Figure 3 (left) is wider than the acquired pulse in Figure 2, and therefore retains the full record of previous pulse widths and appends the new wider pulse value to the existing Trend, showing the history and evolving changes in the measurement.
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Figure 4: The Trend (green) retains a history of
pulse widths, while the Track (blue) shows only a
flat line corresponding to the most recent width. |
Tracks, on the other hand, are not ideal when there is a very low number of measurements per waveform. Shown in Figure 4, a Track and Trend are applied simultaneously to a single pulse. Because the Trend retains the history of pulses measured during previous acquisitions, it is optimal for this type of application. By contrast, the Track operator is synchronized to the same time scale as the acquired pulse and displays a flat line corresponding to the single pulse width which is overwritten with each subsequent acquisition.
Note that this discussion focused only on the Track and Trend of the Pulse Width measurement in order to bring focus to the contrasts between the two math operators, but Tracks and Trends can graph hundreds of other types of input measurement values including Rise Time, Fall Time, Duty Cycle, Skew, Slew Rate, Setup and Hold, custom scripted values, etc., which allows an engineer to characterize a nearly limitless set of varying circuit behavior.
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