Measurement Computing offers a variety of applications and interfaces for its data acquisition systems. MCC provides high-quality hardware with corresponding software and drivers for a quick and adaptable data acquisition solution for your particular application, whether you monitor current, voltage, temperature, strain, or digital signals. To sound more professional in the internet-dominated world, you must work with data acquisition systems or DAQ.
A data acquisition system (DAQ) is anything that records data so you may access observations on other devices. A DAQ performs best while simultaneously keeping track of data using one or multiple devices. Let’s investigate. By 2025, it is anticipated that the global market for data acquisition systems (DAQ) will be worth $2.6 billion, growing at a 5.8% CAGR.
The popularity of business data collection systems in the modern world is influenced by increasing industrial automation and demand for real-time data processing. Gathering data involves using equipment to monitor electrical and physical processes and recording the results on a computer.
The hardware components of the data collecting system include sensors and a programmable software computer. The DAQ’s hardware component provides an interface between the signal and the PC.
Data gathered by DAQ systems are transmitted to a central monitoring and control facility location via instruments and sensors attached to equipment at multiple industrial sites.
What is a DAQ system?
The process of digitizing data from the environment so that it may be presented, examined, and saved in a computer is known as data acquisition, or DAQ, as it is frequently abbreviated. A straightforward illustration uses a sensor like a thermocouple to measure the room’s temperature as a digital value.
Network connectivity, data analysis and reporting software, and remote control and monitoring possibilities can all be added to modern data gathering systems.
The First Data Acquisition Systems
In the early 1960s, the American computer company IBM created machines to store scientific data. The IBM 7700 Data Acquisition System and its successor, the IBM 1800 Data Acquisition and Control System, catalyzed this.
IBM 7700 Data Acquisition System
IBM 1800 Data Acquisition and Control System
These systems were large-scale, expensive computers that needed extensive programming and setup to function correctly because they were created well before the personal computer (PC).
However, they did represent a significant advancement in data recording and were a direct precursor to today’s PC-based data capture systems.
Due to their size, scale, and expense, these IBM data gathering systems were primarily utilized by governments and substantial government contractors, such as NASA and various military branches and contractors.
The First PC-based Data Acquisition
The American company National Instruments Corporation began providing fundamental parts for low-cost personal computers in the middle of the 1980s, including analog-to-digital converter boards (DAQ boards) and GPIB data acquisition cards.
The essential element of this system, aside from the crucial innovation of employing a PC as a data collecting platform, was a piece of software called LabVIEW, which was made available in 1986 for the Macintosh personal computer platform.
Macintosh computer running National Instrument’s LabView programming environment
Engineers might create their data acquisition systems (DAQ systems) using LabVIEW, which comes with an extensive range of built-in features for data processing, analysis, and real-time display on the computer’s monitor.
LabView Programming Environment from Nationa
LabVIEW DAQ software was publishe 1989 as LabWindows/CVI, a DOS-based IBM PC version, to cater to most of the PC market.
When Microsoft created its Windows graphical operating system, the ICM PC platform suddenly gained more graphical capabilities than the Macintosh platform.
LabVIEW for Windows PCs was first made available by National Instruments in 1992 and has remained compatible ever since.
What Does a Data Acquisition System Measure?
Data acquisition systems are principally in the business of measuring physical phenomena such as:
- Strain and Pressure
- Shock and Vibration
- Distance and Displacement
- RPM, Angle, and Discrete Events
Be aware that the data acquisition system can measure additional measurements, such as light and pictures, sound, mass, position, speed, etc.
Components of a DAQ system
All data acquisition systems have four essential elements – Sensor, Signal Conditioning, Analog-to-Digital Converter (ADC), and a Computer with DAQ software (Storage and Display).
All the Real-world phenomena like temperature, force, movement and many more parameters are continuously changing in nature. The measurable quantities that represent the characteristics of any system in nature are called Variables.
The proper functioning of a particular system depends on certain events in time and the parameters of these variables. These variables must be measured with a device that converts the phenomena into a form that humans can perceive.
The conversion devices that translate these physical phenomena into electrical signals (or vice-versa) are called Sensors or Transducers.
Standard sensors include old Mercury Thermometer to Thermocouples, Thermistors, RTDs for measuring Temperature, Load cells for measuring weight and load, Strain gauges for strain on an object (force, pressure, tension…), Microphones for measuring sound, Accelerometers for vibration and shock, and many more.
It’s crucial to consider aspects like the sensor’s accuracy, stability and its behavior in different environmental conditions.
The signal conditioning is necessary to capture a reliable signal when selecting the appropriate sensor for the measurement system.
Additional circuitry is frequently required between the transducer and the ADC to perform high-quality measurements on transducers.
Amplification/attenuation, filtering, Wheatstone bridge completion, excitation, linearization, calibration, and cold-junction compensation (CJC) are some of the circuitry types that can be included in this signal conditioning circuitry.
Different sensors require various types of signal processing. For instance, excitation, bridge completion, and calibration are necessary for signal conditioning for a strain gauge. Before passing through the ADC, the signals from thermocouples, which produce signals in the mV range, must be amplified and filtered.
Signal conditioning circuitry can sometimes be found inside a data-gathering device, but it can also be found inside a transducer. For instance, load cells have amplification, calibration, and bridge completion circuitry.
In addition to signal conditioning, many MEM (micro-electro-mechanical) sensors have it.
An Analog to Digital Converter is the foundation of every data gathering system (ADC). As the name suggests, this chip transforms analog electrical data into discrete levels that processors can understand.
The slightest change in the detected signal that can be detected corresponds to these distinct levels. The more discrete levels used to represent an analog signal and the larger the number of “bits” (12-bit, 16-bit, 24-bit, etc.) of an ADC, the better the ADC’s resolution.
An ADC’s resolution can be compared to a measuring stick’s ticks in many ways. The resolution of a measuring stick with mm tick marks is more significant than one with simple cm tick marks.
Computer with DAQ software (Storage and Display)
The converted variables can be observed in real time through various instruments and displays. But an increasing need of recording and preserving these informations and analyzing them at a later time led to the development of data recorders and data acquisition systems.
Data acquisition systems have developed over time from electromechanical recorders (typically from one to four channels) to all electronic systems capable of measuring hundreds of variables simultaneously.
Early systems used paper charts and rolls or magnetic tape to permanently record the signals, but since the advent of computers, the amount of data and the speed with which they could be collected increased dramatically.
Computers with data acquisition systems will record extremely accurate, repeatable, reliable, and error-free data provided they are connected and operated according to the manufacturer’s recommended practices.
These practices include selecting the correct sensors for the application, the proper wire and shielded cable; capturing the signals in proper magnitude, range, and frequency.
Data Acquisition Types:
The types of DAQs are as follows:
- Analog DAQ
- Digital DAQ
The analog DAQ is a particular kind of device that operates with analog signals and stores information. The following are the fundamental elements of an analog DAQ:
- Transducer for collecting signals and translating them into appropriate media for the apparatus.
- Signal conditioner for boosting and removing noise and ripples.
- Display tool to show off the outcomes.
- Graphic recording instruments for recording the data permanently.
- Magnetic tape instrumentation for acquiring, storing and reproducing data.
The digital DAQ is a system that receives analog signals and uses converted digital signals to function. A digital DAQ’s fundamental parts are as follows:
- A transducer receives analog signals and transforms them into a format that the device can use, like electric
- signal conditioners, to boost weak signals and reduce noise.
- Multiplexers for distributing a single signal over numerous media
- A/D converters transform data into outputs that are appropriate for the device.
- Display gadgets determine the outcomes and visualize the parameters.
- Digital Recorders to store the data in digital format (HDDs, SSDs etc)
The Data Acquisition Systems can also be categorized by its form factor as:
- Rack Mounting Data Acquisition Systems
- Stand-alone Data Acquisition Systems
Rack Mountable Data Acquisition System
The data acquisition systems which are being used to measure multiple parameters from hundreds of sensors in a laboratory or test bench, multiple data acquisition cards are mounted in chassis and these chassis are mounted on a 19” Rack enclosure.
In such a complex environment, rack mounting DAS provides proper distribution of cabling and physical protection to the system.
Logic Fruit Technologies AQUILA Data Acquisition System is an example of Rack mounting Data acquisition system.
Stand-alone Data Acquisition System
Standalone Data Acquisition systems are smaller form factor, few channel, easy plugable data acquisition systems. These data acquisition systems are easy to setup. It can be plugged in to our standard PC with an acquisition application running on it.
AQUILA Standalone DAS from Logic Fruit Technologies is an example of Standalone DAS.
Data Acquisition Options
There are a wide variety of systems to choose from:
Data logging is the process of documenting data gathered over time. The data can be temperature, voltages, current, and humidity readings, depending on the application.
A data logger is a standalone data gathering system that includes an embedded CPU and pre-defined software. Because they are portable, data loggers can function as standalone devices and are widely used.
Every data logger has local storage for data storage, and some include SD card slots for added memory. Data recorders with web configuration and network sharing capabilities are available.
Some data loggers are battery-powered for even more portability.
Data Acquisition Devices
Signal conditioning and an analog to digital converter are features of data capture devices (USB, Ethernet, PCI, etc.), but they require a computer to work.
These devices are a popular option since they are versatile and valuable in many applications.
Users of plug-in devices can choose between a programming environment like PythonTM, C++®, DASYLab®, MATLAB®, and NI LabVIEWTM or pre configured data acquisition software like DAQamiTM.
Data acquisition devices provide a flexible solution for your particular application with various BUS options and the freedom to use your favorite software.
Data Acquisition Systems
Systems with a high channel count and complexity that combine and synchronize many sensor types are built for modular data gathering.
Although more challenging to use and integrate, these systems are very adaptable.
The most expensive data acquisition option is a modular system, yet many applications need the characteristics that a PXI-compatible data acquisition system can only offer.
Measurement Process of DAQ
Real-world signals are translated into the digital realm during data capture to display, store, and analyze.
Actual phenomena must first be measured in the analog or the physical world we live in before being transferred to the digital domain.
Numerous sensors and signal conditioning circuitry are used in this procedure.
Analog-to-digital converters (ADCs) sample the outputs before writing them in a time-based stream to the previously described digital memory medium. These systems are commonly referred to as measurement systems.
A complete scheme of an analog data acquisition system
Let’s examine each of these links in the chain individually.
- Sensors or Transducers
- Signal conditioners
- Analog-to-digital converters (ADCs)
- Data storage
- Data visualization
- Data analysis
Sensors or Transducers
A sensor is the first step in measuring a physical phenomenon by continuous motion, such as temperature, sound level, or vibration.
A transducer is another name for a sensor. A sensor transforms an observable physical phenomenon into an electrical signal.
Everyday activities include the usage of sensors. For instance, an ancient sort of sensor used to measure temperature is the standard mercury thermometer.
It uses colored mercury in a closed tube and depends on the fact that this chemical responds consistently and linearly to temperature variations.
We can only reasonably accurately determine the temperature by looking at the thermometer by marking the tube with temperature numbers.
The classical thermometer has been used to measure temperature for centuries.
Of course, there is only a visual output and no analog output. Although handy in the oven or outside the kitchen window, this simple thermometer is unsuitable for data collecting.
Thermocouples, thermistors, RTDs (Resistance Temperature Detectors), and even infrared temperature detectors are different types of sensors developed to measure temperatures.
Millions of these sensors are used daily for various purposes, including measuring temperature in pharmaceutical manufacturing and displaying engine temperature on dashboards.
Temperature measurement is used in almost every industry.
Temperature sensors: from left to right: thermocouple, thermistors, RTD sensor
Of course, numerous additional types of sensors have been developed to gauge various other physical phenomena.
- Load cells: for measuring weight and load
- LVDT sensors: LVDTs are used to measure displacement in distance
- Accelerometers: measuring vibration and shock
- Microphones: for measuring sound,
- Strain gauges: to measure strain on an object, e.g., measure force, pressure, tension, weight, etc.,
- Current transducers: for measuring AC or DC,
- and countless more.
The electrical output of a sensor can be a voltage, current, resistance, or another electrical characteristic that changes over time, depending on the type of sensor.
The input of a signal conditioner, which we will discuss in the next section, is often connected to the output of these analog sensors.
Signal conditioners’ job is to take the output from analog sensors and get it ready for digital sampling.
Suppose we stick with the thermocouple as an example.
The sensor’s output needs to be linearized by the signal conditioning circuitry, which also needs to provide isolation and amplification to raise the very low voltage to a usable level for digitizing.
From analog signal sources to digitalized data ready for processing by computer and software
Ref Link: Source
The manufacturer builds each signal conditioning circuitry to carry out the fundamental normalization of the sensor output, guarantee its linearity and fidelity to the source phenomena, and get it ready for digitizing.
Additionally, because each type of sensor is unique, the signal conditioners must adapt to each type of sensor flawlessly.
Isolation Barriers (Galvanic Isolation)
Electrical isolation, also called galvanic isolation, separates a circuit from other sources of electrical potential.
This is crucial for measuring systems since most signals are present at relatively low levels and can be strongly impacted by external electrical potentials, leading to inaccurate measurements.
Both AC and DC potentials can act as interfering forces.
For instance, placing a sensor directly on a test object (such as a power supply) that has potential above ground (i.e., not at 0V) might cause a signal to be DC-offset by hundreds of volts.
AC signals produced by other electrical components in the signal path or the vicinity of the test site might likewise be considered electrical interference or noise.
For instance, the room’s fluorescent lights can emit 400Hz, which sensitive sensors can detect.
Because of this, the best data collection systems include separated inputs to protect the signal chain’s integrity and guarantee that the sensor outputs match what has been read. Today, several different types of isolation techniques are used.
Electrical interference or noise can impact almost any signal we seek to measure. This can be caused by several things, such as simple voltage potentials between the sensor or measurement device and the object being tested or ambient electromagnetic fields that can be produced into high-gain signal lines.
To eliminate these interferences and improve measurements, the best signal conditioning systems include adjustable filtering that the engineer can utilize.
In this scheme, a noise analog signal is passed through a low pass filter to filter unwanted frequencies.
Filters are usually expressed in terms of the band that they operate upon. There are four basic types of signal filters:
- Low-pass filter: this filter reduces or “rolls off” starting at a given frequency and those above it.
- High-pass filter: does the opposite and allows frequencies to pass above a given frequency.
- Band-pass and band-reject filters: pass or stop (reject) frequencies between two given values.
Analog-to-Digital Converters (ADCs or AD Converters)
Analog signals are typically produced as the output of physical measuring signal circumstances.
This signal must be transformed into a succession of fast digital values so that the data acquisition system can display and store it.
To transform this signal, an A/D card or A/D subsystem is employed.
AD converter scheme – converts the analog signal into digital domain data
ADCs can be multiplexed or single converters per channel, among other variations. A single analog-to-digital converter is used in a multiplexed ADC system to convert numerous signals from the analog to the digital domain.
This is accomplished by multiplexing the analog signals into the ADC one at a time.
This method is less expensive than using one ADC chip for each channel.
On the other hand, because only one signal can ever be translated at a time, it is impossible to accurately align the signals on the time axis. A timing skew between channels, therefore, always exists.
8-bit ADCs were widely used when data capture first started. As of this writing, most data acquisition systems intended for dynamic measurements use 24-bit ADCs as standard, and 16-bit ADCs are frequently regarded as the absolute minimum resolution for signals in general.
The sample rate refers to the speed at which the signals are transformed.
Since the measurements fluctuate slowly in some applications, a high rate is unnecessary, like most temperature measurements.
However, many other measurands, such as AC voltages and currents, shock, and vibration, demand sample rates of at least tens of thousands of samples per second. The T or X-axis of measurement is regarded as being the sampling rate.
Modern data acquisition systems generally transfer data from the ADC subsystem to long-term storage using a solid-state hard disc drive (SSD or HDD). The data can be examined after the test by writing it to a disc.
Most DAQ systems enable data export to several file formats for analysis using external software applications. Standard data formats include UNV (Universal File Format), CSV (Comma Separated Values), and others.
Data Visualization and Display
The capacity to visualize the data in real time while it is being stored is one of the most critical features of any DAQ system.
Systems often use a flat-screen display, either integrated or independent, that may be set up in different visual forms.
Waveform data can almost always be shown in numerical form and as Y/T waveforms across a graph or grid.
Using bar graph meters, FFT (Fast Fourier Transform) frequency/magnitude graphs, and other graphical standards is also possible.
- Recorders: horizontal, vertical, and XY recorder
- Oscilloscope: scope, scope 3D, vectorscope
- FFT: FFT, 3D FFT, Harmonic FFT, and Octave
- Meters: digital, analog, horizontal/vertical bar meters
- Graphs: 2D, 3D graph, Octave, Orbit, Campbell plot
- Video: standard video display and thermal video display with temperature indicators
- GPS: positioning display with interactive Open Street Map layering support
- Control: button, switch, knob, slider, user input
- Combustion analysis: P-V diagram and combustion scope
- Rotor balancer: for field balancing
- Automotive: 3D polygon for displaying moving objects
- Aerospace: altitude or artificial horizon indicator
- DSA/NVH: Modal circle
- Other: 2D/3D table, image, text, line, overload indicator, indicator lamp, note
Data acquisition systems offer an essential visual reference for the real-time status of the test.
Data analysis tools that are either included in the DAQ system itself or that are part of outside data analysis software can be used to study the data after it has been stored in the DAQ system.
As was already said, the vast majority of DAQ systems currently available on the market have several built-in data export filters that transform the system’s proprietary data format into other data formats for offline analysis.
Various Display and Analysis windows of AQUILA DAS ACQ Software
Factors affecting the measurement accuracy
The quality of the measuring tool is only one of many variables that affect measurement accuracy. As a result, it is usually recommended that the user take readings in a highly neutral environment.
The following list includes several typical disturbances that have an impact on measurement accuracy:
- Temperature: Most of the sensors we employ, particularly piezoelectric, are somewhat temperature-dependent. Techniques for temperature correction are utilized to offset this effect.
- Calibration Errors: Calibration is required once a DAQ system has been configured to provide the most precise readings. Without calibrating the measurement apparatus, you’ll start getting inaccurate readings.
- Human Errors: One of the most frequent errors seen while taking measurements is human error, ranging from forgetting to perform an essential step during the measurement to a poor test setup. For this reason, most experiments and tests come with a protocol. Any experimental observation must be carried out following it to be effective.
- Humidity: It’s generally not a good idea to take measurements when it’s excessively humid since electrical components can fail when the humidity is too high.
- Effect of Friction, Hysteresis, or Other Electrical Noise: Electricity’s magnetic properties can significantly impact the nearby object, especially if it’s made of metal. Measurement errors may result from taking measurements while overlapping electrical noise is present.
- Vibrations: Sensor readings can be affected by vibrations unless you are explicitly measuring them! The test bench must be steady and free from unwelcome vibrations or friction.
- Light: Light may seem like an odd error, but if you are measuring the intensity of light coming from a source and there is another light leakage, it might result in major measuring instabilities.
- Nonlinearity: Nonlinearity error happens when the sensor output curve doesn’t vary proportionally to the input curve. This shows that the measuring device cannot scale appropriately with a different load or input.
Benefits of using DAQ devices
Let’s discuss the benefits of using a Data Acquisition device for measurements.
- DAQ Systems are Industry-Leading Measurement Devices
The fact that you are employing a measurement method that is widely used is the main advantage. A DAQ gadget is an excellent option for engineers, researchers, and scientists who want to collect accurate data on their tests because of its capacity to link various sensors and function in conjunction with modern computers.
- DAQ Systems are Highly Versatile
DAQ systems are offered as integrated measurement systems with various measurement modes that can measure various attributes. Additionally, they are created as specialized tools that can only measure a particular property.
- Documentation – Eliminates Data Redundancy
Documentation capacity is one of a DAQ system’s distinguishing characteristics. With the computer automatically recording the data, you may run various tests one after the other without having to write down the results or worry about forgetting the readings.
When you purchase a DAQ system that can measure a subject’s many attributes, you get rid of many standalone devices. The overall cost of your test setup is reduced as a result.
- Better File Processing and Transfer Capabilities
The computer stores the data gathered by a DAQ system, which is then processed or evaluated at the user’s leisure. A computer also makes it simple to transfer data to other devices.
Important applications of the DAQ system
Many different industries make use of DAQ systems. Let’s talk about a few of these computer-based measurement systems to grasp the adaptability of these computer-based measurement systems.
- Electronics: Electronics is among the leading examples of a sector where DAQ implementation is standard. The DAQ systems are used to evaluate various factors, including heat production, conductivity, resistance, magnetics, and more, involved in the design of electronics.
- Automotive Industry: DAQ machines are frequently seen in automotive manufacturing facilities to check the quality of the produced parts.
- Imaging: DAQ devices are frequently used for quality testing image equipment, including cameras, lenses, and scientific equipment like scanners and microscopes.
- Laser Technology: Testing laser performance, light intensity, and color can be done with DAQ devices.
- Sonar – Radar: testing the effectiveness and efficiency of remote sensing systems like radar and sonar.
- Industrial Machines: Because industrial devices are designed to carry out a procedure repeatedly, repeatability is crucial. DAQ is frequently used to test the machines’ resistance to repetitive stresses.
- Non-destructive Testing (NDT): The analysis of acoustic emission phenomena, seismology, geology, ultrasonic measurements, and non-destructive testing of structures all use DAQ equipment.
Purpose of Data Acquisition
A data acquisition system’s primary function is to gather and store data. However, they are also designed to offer data visualization and analysis in real time and after recording.
Additionally, most data collecting systems include built-in analytical and report production capabilities.
Combining data acquisition and control, where a superior DAQ system is closely coupled and timed with a real-time control system, is a recent breakthrough.
The article “Merging Data Acquisition with a Real-Time Control System” has more information on this subject.
Engineers in various applications have varied needs, of course, but these essential talents are present in different degrees:
- Data recording
- Data storing
- Real-time data visualization
- Post-recording data review
- Data analysis using various mathematical and statistical calculations
- Report generation
LFT’s AQUILA data acquisition system offers a complete data recording, storing, visualization, analysis, and reporting solution in one complete package.
Data acquisition instrumentation is also heavily used in monitoring applications. Such examples are:
- Monitor the condition of complex machinery such as generators, motors, fans, etc.
- Monitor structural properties of buildings such as bridges, stadiums, etc.
- Monitor energy consumption and energy efficiency in the production process.
- And many other monitoring scenarios.
A DAQ may adapt to any user’s desired operation, whether a simple procedure like a drill machine or a complex one. All of this can be done without worrying about expensive fancy expenses. A DAQ is cost- and time-effective, doesn’t suffer significantly from variables, and even if it does, there are various ways to stop them in their tracks. Since the beginning of time, a DAQ has been frequently utilized since it gave incredibly accurate data, and throughout time, it has become compatible with sophisticated technologies.