Data acquisition can be defined as the process of sampling signals that measure real-world physical conditions and convert the resulting samples into digital numeric values that can be manipulated by a computer or software. Data acquisition systems, abbreviated by the initialisms DAS, DAQ, or DAU, typically convert analog waveforms into digital values for processing.
The Components Of Data Acquisition Systems Include:
- Sensors - they convert physical parameters to electrical signals.
- Signal conditioning circuitry - that converts sensor signals into a form that can be converted to digital values.
- Analog-to-digital converters that - convert conditioned sensor signals to digital values
The data acquisition process has two different approaches. One is the traditional approach that uses external sensors such as cameras or other monitoring devices. The other one is the newly introduced approach which uses wearable wireless sensors.
The CEO of dissertation writing services firm said that data acquisition applications are usually controlled by software programs developed using various general-purpose programming languages such as Assembly, BASIC, C, C++, C#, Fortran, Java, LabVIEW, Lisp, Pascal, etc. Stand-alone data acquisition systems are often called data loggers.
Data Acquisition is accepted to be distinct from earlier forms of recording to tape recorders or paper charts. The signals are converted from the analog domain to the digital domain and then recorded to a digital medium such as ROM, flash media, or hard disk drives.
Components Of The Data Acquisition SystemModern digital data acquisition systems consist of four essential components that form the entire measurement chain of physics phenomena. They include:
- Signal Conditioning
- Analog-to-Digital Converter
- Computer with DAQ software for signal logging and analysis
Data Acquisition System Works By Measuring Physical Phenomena Such As:
- Strain and pressure
- Shock and vibration
- Distance and displacement
- RPM, angle, and discrete events
- Light and images
Purposes of Data AcquisitionThe main idea behind using a data acquisition system is to acquire and store the data. At the same time, these systems also intend to provide real-time and post-recording visualization and analysis of the data. In addition to this, most data acquisition systems have some analytical and report generation capability built-in that helps them gather the most accurate data as it occurs.
The most recent development in this field is the combination of data acquisition and control, where a high-quality DAQ system is connected tightly and synchronized with a real-time control system.
Engineers in different applications have various requirements depending on their field of work and the data it requires, but these key capabilities are present in varying proportion:
- Data recording
- Data storing
- Real-time data visualization
- Post-recording data review
- Data analysis using various mathematical and statistical calculations
- Report generation
The good thing about data acquisition instruments is that they can be most effectively used for monitoring applications too. They can be used for:
Monitoring the condition of complex machinery such as generators, motors, fans, etc.
Monitoring structural properties of buildings such as bridges, stadiums, etc.
Monitoring energy consumption and energy efficiency in the production process
What Makes Data Acquisition Systems so Important?Data acquisition systems or devices play a very significant role in the testing of products. They can be anything, from automobiles to medical devices or industrial equipment. It could be any electromechanical device that people use.
Before data acquisition, products were tested in an unstructured, highly subjective manner. When testing a new suspension in an automobile, engineers often relied on the opinions of test drivers as to how the suspension “felt” to them as there was no other way to determine its efficiency.
As the data acquisition systems continue to develop and evolve, they can collect data from a wide variety of sensors. They help to collect data from a wide variety of sensors, and these types of subjective opinions were replaced with objective measurements. These could easily be repeated, compared, analyzed mathematically, and visualized in many ways.
The Measurement ProcessData acquisition can be rightly called the process of converting real-world signals to the digital domain for display, storage, and analysis. As the physical phenomena exist in the analog domain, i.e., the physical world that we live in, they must be first measured there and then converted to the digital domain.
This process is carried out using a variety of sensors and signal conditioners. The outputs are sampled by analog-to-digital converters (ADCs) and then written in a time-based stream to a digital memory media, as mentioned above. Such systems are known as measurement systems.
Data acquisition sensors to measure another physical phenomenon
Many types of sensors have been invented to measure another physical phenomenon. They include:
- Load cells that are used for measuring weight and load
- LVDT sensors that help to measure displacement in distance
- Accelerometers that are used for measuring vibration and shock
- Microphones that measure sound
- Strain gauges for measuring strain on an object, e.g. measure force, pressure, tension, weight, etc.
- Current transducers for measuring AC or DC
Data acquisition has become easy and effective as the systems used for acquiring data make use of a solid-state hard disk drive to stream data from the ADC subsystem to permanent storage. After it has been stored in the DAQ system, the data can also be analyzed using tools, either built into the DAQ system or third-party data analysis software. It makes it easy for the researchers to analyze the data after the test is completed.
Also, with the help of modern technology, the flexible DAQ systems allow the users to configure one or more displays freely, efficiently using the built-in graphical widgets. These days nearly every DAQ system on the market today offers several built-in data export filters that convert the system’s proprietary data format to third-party data formats for offline analysis.