A data acquisition system consists of many components that are integrated to sense physical variables, by using transducers to convert the physical variable to an electrical signal, then condition that electrical signal to make it readable by an analog-to-digital converter.
After being converted by the A/D, a computer can then read that data and process, analyze, store and display the acquired data with the help of software.
This is a block diagram of the steps needed to take a physical variable and make it usable by a computer. The stages of this system will be used in various labs throughout the quarter. Next, we will explain each of these stages in more detail.
Transducers are used to sense physical phenomena and translate it into electric signals. Examples of things that can be measured with transducers are temperature, pressure, light, force, displacement, level, electrical signals, and switches.
In the signal conditioning stage, electrical signals are conditioned so they can be used by an analog input board. The signal may be conditioned by amplification, where the power of the signal is increased to make it easier to read in more detail. Isolation may also occur, so that the input and output circuitry do not interfere with each other. The signal may also be filtered, to remove noise or unwanted frequencies from the signal. Linearization happens to help make the output proportional to the input.
The analog to digital converter must take into account many factors, such as input signal, sampling rate, throughput, resolution, range, and gain. These will all be discussed shortly.
An A/D converter takes a a continuous analog input signal, such as a strain gage, and converts it to a digital signal. A digital signal is either on or off, like a light switch.
One of the factors to consider in converting analog signals to digital is the sampling rate. The sampling rate determines how often conversions take place. The higher the sampling rate, the better. This example shows three different sampling rates for an analog input signal. The 16 samples per cycle digitized signal looks closer to the original analog input than the 4 samples per cycle signal. The reason one would use a lower sampling rate is because the amount of total samples that can be taken is limited, the processing power required to handle that much data is limited, or the extra precision obtained by the high sampling rate is unnecessary.
A problem with using too low of a sampling rate is that aliasing might occur. Aliasing is when the acquired signal gets distorted by a sampling rate that is too small. In this example, the original signal is sampled so slowly that the sampled signal looks like a completely different frequency than the original signal.
Another factor to consider with A/D conversion is throughput. The effective rate of each individual channel is inversely proportional to the number of channels sampled. For example, if the total maximum throughput of a system is 100 kilohertz and 16 channels of data must be used, then the total throughput for each channel is 6.25 kilohertz.
The range of an A/D converter is the minimum and maximum voltage levels that the A/D converter can quantize. Ranges are selectable either through hardware or software to accurately measure the signal.
The resolution of a converted signal is the number of bits that are used to store each sample of data. For example, a two bit resolution will allow 2 to the 2nd power number of values for the data, meaning that the data can take 4 possible values. For 3 bit conversion, 8 values are possible, and for 5 bit conversion, 32 values are possible. The higher the resolution, the closer the digitized signal will resemble the original analog waveform.
Data acquisition software can be the most critical factor in obtaining reliable, high performance operation. Software transforms a personal computer and data acquisition software into a complete acquisition, analysis, and display system. There are different types of data acquisition software… custom programmable software and pre-built data acquisition software packages.
Programmable software involves the use of a programming language, such as C, C++, Fortran, or many other languages. The advantage of writing custom software for data acquisition is that the programmer has ultimate flexibility in what the software does. The disadvantage is that this is complex and has a steep learning curve.
The alternative is to use data acquisition software. This does not require traditional programming. Developers can design custom instruments best suited to their application. Examples are Testpoint, SnapMaster, LabView, DADISP, DASYLAB, and others.
There are many factors to consider while designing a Data Acquisition System. Is it a fixed or a mobile application? What is the type of input/output signal: digital or analog? What is the Frequency of input signal ? What is the needed Resolution, range, and gain? Is it important to have Continuous operation? Is there Compatibility between hardware and software. Are the drivers available? What is the Overall price of the system?
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