• Minimum or maximum value of a 50, 60, or 400 Hz power waveform
• Minimum, maximum, or average acceleration (g) from an accelerometer for vibration studies
• Maximum (systolic), minimum (diastolic), and average (mean) of a pulsatile blood pressure waveform in life sciences research
• Elimination of noise on near-DC signals (e.g., thermocouples)
• There/not-there recordings of high-frequency waveforms (e.g., audio, vibration, noise)

One of our customers wanted to instrument his dynamometer. The parameters he needed to measure included oil pressure, rpm, torque, and engine temperature-all low-frequency waveforms. The problem was a fifth parameter, vibration, derived from an accelerometer mounted to the engine block. This signal produced high-frequency information compared to the other four, and forced some unusual conditions on the measurement approach as a result. He had the following options:

• Sample all channels at a high rate consistent with the frequency response requirements of the accelerometer.
• Sample the four lower frequency channels at a slow rate and the fifth high-frequency channel at a much faster rate.
• Determine how often it is necessary to report each channel's value (say, five times per second, or 5 Hz) and select that sample rate.

The second option is DA's holy grail-selectable sample rates per channel-and is offered by a handful of DA systems. But be prepared to pay dearly for the feature. For the sample rates we're discussing here, system prices range from $20,000 to well over $40,000.

The third option doesn't seem grounded in reality. How can you sample a fast-moving channel at a slow rate and derive any meaningful information? The key is in the application and the sampling approach you use.

From the perspective of the application, our customer wasn't interested in the actual waveform produced by the accelerometer (see Figure 1). He didn't need a continuous vibration signal that he could, for example, transform with a FFT to determine all its frequency components and magnitudes. He simply wanted to know the maximum g's produced by the vibrating engine over a unit of time. And in the context of his application, he wanted the value reported five times per second. In other words, he wanted to accumulate acceleration data for 200 ms (1 divided by 5 Hz), then report only the maximum value.

Intelligent Oversampling Is the Answer
Since a continuous reproduction of the high-frequency waveform is not required, we can exploit a little-known technique called oversampling. Many DA products support dual sample rate capability where the DA hardware samples data at a much faster rate than is reported to the software. Also known as burst sampling, the technique is most often used to minimize time skew by sampling all enabled channels at a high rate. Higher burst rates yield smaller time skew errors. But for DA products with onboard intelligence, yet another benefit emerges from oversampling: the ability to evaluate and apply all the data that are typically thrown away. This is the acquired channel information orphaned by the software because it needs data much less frequently than the hardware's burst rate provides them. I'll clarify the concept, called intelligent oversampling (IOS), using our dynamometer example.

The application assumes that a sample rate of 5000 Hz is adequate to capture the peak g values generated by the accelerometer. This, the rate of the fastest moving signal with respect to time, forms the basis used to calculate the required burst rate of the hardware. Since five channels need to be acquired, the burst rate is 25,000 sps (5000 Hz · 5 channels), as shown in Figure 2. Remember that 25,000 Hz represents the rate that our hardware is continuously scanning our five enabled channels regardless of how often the application software requests conversions. In the context of our application, the software will be programmed to acquire data at 5 Hz/channel. Calculating a throughput number for it yields 25 Hz (5 Hz · five channels). At this point it's clear that the hardware generates excess data at a ratio of 1000:1 (25,000 Hz 4 25 Hz). What happens to the data?

The correct answer for most hardware is "nothing." It simply takes the 1000th point (which we refer to as the last point) and reports that value to the software-ignoring the other 999 in the process. But DA hardware products with IOS put the excess samples to work. For our accelerometer channel, they can evaluate the 1000 samples converted each 200 ms and report the maximum value. This approach yields a stream of data values at 5 Hz that precisely describes the peak envelope of the accelerometer waveform-exactly what the application demanded. And we're achieving this at only a 25 Hz effective sample rate which, over the 8-hr test, consumes only 1.4 MB of disk space-three orders of magnitude less than our first option. But there's more to this story.

Turn your attention to the other four channels of the application. What advantage does IOS offer them? It doesn't make any sense to capture the maximum or minimum values of these near-DC signals. But an arithmetic average calculation can yield significant noise reduction. Every 200 ms the 1000 values acquired from each of the four channels is averaged to a single data value. The result is a waveform where noise reduces toward zero to cleanly reveal even the most minor fluctuations in magnitude (see Figure 3).

There are some cautionary notes regarding IOS. Its effectiveness degrades as the software's sample rate approaches the burst rate of the hardware. Selecting the average mode when the hardware and software rates are 20,000 and 10,000 Hz respectively yields a virtually useless 2-point average. Another consideration is to ensure that the software's rate is below the highest frequency component of the signal you're measuring. Measurements off a 60 Hz power line, even when the hardware rate is significantly higher than the software rate, will be distorted when the software rate is $60 Hz. This situation creates a dilemma when the software attempts to report a minimum, maximum, or average value before the signal is able to complete one cycle.

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