This article will take an in-depth look at data acquisition systems.
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This chapter delves into the foundational aspects of data acquisition systems, discussing their essential components and the processes involved in gathering and analyzing data.
A data acquisition system is a cohesive assembly made up of sensors, measurement tools, and computers. Its primary function is to collect and analyze data to provide insights into electrical or physical phenomena, offering a thorough interpretation of the gathered information.
To comprehend how a data acquisition system functions, it is essential to understand its data processing and recording mechanisms. For example, data acquisition systems can measure the temperature of a heating coil, ensuring it heats an object to a precise temperature. This assessment of the coil’s efficiency is achieved by examining its temperature, a process termed data acquisition, enabled by the system. Similarly, these systems measure voltage drops across electrical resistors.
The key objective of recording and measuring electrical and physical phenomena via data acquisition systems is to facilitate in-depth analysis. Utilizing specialized software, these systems can efficiently process and store data in various user-friendly formats. They capture real-world data and preserve it for further scientific or engineering evaluation.
Data acquisition systems come in handheld and remote variants. Handheld models are ideal when direct interaction with the specimen is feasible, while remote systems are suitable when in-person interaction is not possible or needed, allowing measurements from afar.
The data collection process starts by identifying the physical attributes to measure. Measurements could involve temperature, light intensity, vibration, pressure, fluid dynamics, and force, among others. Any physical property measured first needs conversion into a format suitable for the data acquisition system to sample.
This conversion occurs through sensors. A data acquisition system integrates both hardware and software to measure or manage real-world physical properties. A comprehensive system includes DAQ hardware, sensors, actuators, signal conditioning equipment, and computers running DAQ software. If timing precision is necessary, especially in event-mode DAQ systems, an independent timing mechanism might be essential.
Sensors, or transducers, facilitate interaction with the measured subject, either directly or indirectly. They convert physical values into electrical signals. Various sensors are employed in data acquisition systems, depending on the application. For instance, a temperature sensor measures heat, while a photovoltaic sensor measures light.
All sensors serve the common purpose of transforming analog signals—like temperature, light, and speed—into digital signals for computer processing. The high quality of sensors in DAQ systems ensures precise readings with minimal noise or disruption.
Sensor-derived electrical signals might include noise or interference and may need modification for use. Often, these signals are too weak for accurate measurement by the data acquisition system. To counteract these issues, a signal conditioner is used. Signal conditioning refines and optimizes signals for precise measurement and trustworthy data collection.
Signal conditioners utilize filter circuits to separate noise from genuine signals and amplification circuits to strengthen weak signals. These functions are essential to the role of signal conditioners. Additionally, a well-designed circuit can perform tasks such as linearization, calibration, and excitation. The appropriate choice of conditioning circuit aligns closely with sensor characteristics in the DAQ system.
Serving as a bridge between sensors and computers, data acquisition hardware connects to computers via USB ports or PCI-express slots. This hardware receives signals from sensors and converts them into a digital format that computers can interpret, enabling data processing and analysis.
The Analog-to-Digital Converter (ADC) is integral in transforming analog signals into digital data. At the core of every DAQ system, the ADC converts environmental data into discrete levels for processor interpretation. These levels represent the smallest detectable changes in the measurement.
An ADC's resolution is defined by its bit depth—a higher bit count offers more discrete levels, enhancing the accuracy of conversion. This resolution mirrors precision markings on a measuring device.
For instance, a ruler with millimeter markings offers finer resolution than one with only centimeter markings. Similarly, in the U.S., a yardstick marked in inches allows greater detail than one marked solely in feet. The required resolution aligns with specific measurement needs, much like selecting the appropriate ADC resolution for an application.
This DAQ system component facilitates the input and output of binary signals, ensuring the smooth exchange of digital data between the system and external devices or components.
These converters support single-ended wire connections for signal measurement and interface, making them integral to the data acquisition process.
Certain DAQ hardware systems are designed to function independently without needing a computer. These standalone systems feature embedded processors and computing modules within the hardware, allowing real-time data representation independently. Standalone examples include oscilloscopes and data loggers, which measure and document specific data without external computing resources.
The sampling rate, or frequency (F), is influenced by the monitored process's characteristics and practical concerns. In digital data acquisition, transducers convert continuous analog signals to digital formats for processing. Since computers can't store analog waveforms, signals are divided into discrete samples. The sampling rate, measured in hertz, indicates the number of samples taken over time from a continuous signal.
The sampling rate is critical for accurately interpreting trends. A low sampling rate might miss or distort significant trends. Although past limitations in computer storage and memory were concerns, modern computers generally possess sufficient capacity for high sampling rates.
Grasping the concept of sampling rates is essential for robust data acquisition. Clients may require high sampling rates for short-term tests to capture quick fluctuations. Conversely, longer data analysis might favor one-second or less sampling intervals, with storage methods tailored to client needs.
In a data acquisition (DAQ) system, the computer's role is pivotal as the final component. Its primary function is to collect and process data from the DAQ hardware for subsequent analysis. Merely connecting the hardware to a computer isn't enough for data interpretation. Specialized DAQ software is necessary for translating raw data into readable, meaningful results. This software acts as an intermediary between the hardware and user, enabling the computer to execute advanced analyses based on the gathered data.
This comprehensive chapter explores the essential measurements, modules, and methods utilized in modern data acquisition systems (DAQ). You’ll discover how industrial and scientific DAQ solutions collect, process, and analyze data, leveraging key data acquisition components such as sensors, signal conditioning modules, transducers, and software. We’ll cover the full spectrum of techniques and digital data acquisition tools that enable precise, reliable, and efficient real-time monitoring—helpful for applications from industrial automation to research data logging.
Data acquisition systems are engineered to capture and digitize a vast range of physical phenomena and process parameters, usually beginning as analog signals. These analog inputs—such as voltage and current—are vital for process control and monitoring, requiring conversion to digital signals through analog-to-digital converters (ADC) for further analysis by computers and programmable logic controllers (PLCs).
Common measurement parameters in industrial and research DAQ systems include, but are not limited to:
Specialized DAQ devices use various sensor interfaces—including analog input modules, digital modules, and universal input modules—for signal conditioning and adaptation. Signal conditioning is an essential part of measurement accuracy, ensuring proper amplification, filtering, and isolation of sensor outputs. Often, sensors offer voltage or current signals that are calibrated and scaled to represent the measured physical variable. For example, temperature sensors like thermocouples output millivolt signals, which are amplified and converted to meaningful temperature values within the DAQ system.
Highly flexible multi-channel DAQ modules can accommodate a wide mix of sensor types and measurement ranges, making them suitable for laboratory test benches, industrial process control, and field data logging. For challenging or custom applications, tailor-made DAQ hardware and modular systems can be developed, integrating custom sensor arrays or protocols—such as CAN bus or Modbus—for targeted measurement solutions.
Many advanced data acquisition systems utilize modular, rack-mounted platforms filled with a selection of function-specific DAQ cards. These cards may include analog input cards, analog output cards, digital I/O cards, and specialized counters—each conforming to established bus standards such as PCI, PXI, or USB DAQ interfaces. Standardization increases compatibility and interchangeability of modules across multiple manufacturers, simplifying system expansion and maintenance. These modular approaches provide scalability and flexibility, supporting large-channel count configurations and future upgrades, which are critical for scalable test and measurement environments.
Robust data acquisition software is crucial for end-to-end control, visualization, and analysis of measurement data. It offers user interfaces for configuring data channels, setting logging intervals, and managing triggers and alarms. DAQ software may be custom-developed (using platforms like LabVIEW, MATLAB, or Python with DAQ libraries) or purchased as turnkey, pre-built software packages. Key functions usually include real-time data logging, visualization dashboards, automated data export, and integration with enterprise systems, such as SCADA or MES. Modern DAQ software often supports remote monitoring via cloud connectivity and IoT frameworks, enabling access to real-time results from anywhere.
Off-the-shelf, proprietary DAQ software packages are widely adopted due to their reliability, support, and reduced maintenance requirements compared to fully custom solutions. These platforms handle common needs such as device drivers, calibration routines, and regulatory compliance (e.g., FDA CFR 21 Part 11 for data integrity). Companies typically evaluate key features such as data security, user access control, compatibility with various hardware, and long-term support when choosing the optimal DAQ software for their workflow.
Transducers play a critical role in DAQ systems by converting various forms of physical energy—such as heat, pressure, or movement—into electrical signals suitable for measurement and analysis. The output of a transducer can be categorized as either analog or digital, defining the DAQ system’s input requirements. For example, analog output transducers (such as load cells, thermocouples, or piezoresistive sensors) require high-resolution ADCs, while digital output transducers (such as quadrature encoders or smart sensors) need high-speed digital input capabilities. The DAQ system’s signal conditioning modules are often tailored to match these sensor interfaces, improving accuracy and minimizing noise, especially in harsh industrial environments.
Multiple acquisition methods are leveraged in DAQ platforms, depending on the application, level of detail needed, and industry standards for data integrity and traceability. These include:
This method is vital in digital forensics and secure archival. It consists of creating a complete, sector-by-sector forensic image of a storage device, preserving metadata and non-visible (deleted or hidden) files. The resulting image can be analyzed or restored independently of the original device. Industry-standard tools—such as EnCase, X-Ways, FTK, and ILook Investigator—are commonly used for this advanced data capture technique.
Where software or hardware issues prevent imaging, bit-stream disk-to-disk cloning is performed. This technique produces an exact, bootable clone of the source storage device, maintaining original geometry and data structure. This method is indispensable for system recovery, legacy hardware duplication, and IT asset management. Forensic and IT professionals employ tools like EnCase, SafeBack, and Norton Ghost to support this process.
Logical acquisition supports selective data collection, focusing on files, directories, or system areas critical to an investigation or process. Typical uses include acquiring email databases (.ost, .pst), patient records, or other targeted file types from large servers or storage arrays. This approach streamlines analysis by excluding irrelevant system files.
Sparse acquisition refines logical acquisition by targeting only relevant data fragments or current unallocated space, reducing data volume in legal and forensic review while still enabling recovery of deleted data if preserved in available sectors.
Effective DAQ system design and setup involves advanced planning around technical specifications, application requirements, and budget. Key considerations include:
Determining acceptable continuous operating periods is essential for applications like long-term environmental monitoring or manufacturing test stations. Extended runtimes increase the risk of buffer overflow and necessitate robust disk storage, redundant power (including uninterruptible power supply/UPS), and reliable thermal management. These engineering choices help prevent data loss, downtime, and system fatigue over prolonged operation.
The selection of an appropriate power supply is foundational for DAQ reliability. Battery-powered, solar-powered, or line-powered DAQ systems must be matched to deployment environments—ranging from remote outdoor monitoring to industrial plant operations. Incorporate backup solutions, such as generators or hot-swappable batteries, and regularly assess power consumption to ensure uninterrupted operation.
Analyze the means by which the DAQ system will transmit or store measurement data. Options include wired (Ethernet, USB, CAN bus) and wireless (Wi-Fi, cellular, Zigbee, LoRa) communication for local or cloud-based storage. Calculate expected data rates, plan for adequate local buffer memory, and implement store-and-forward functionality for mission-critical applications. Ensure compliance with cybersecurity standards for remote and networked DAQ deployments, especially when integrating with SCADA or industrial IoT ecosystems.
Remote or multi-user access is a frequent requirement—especially in distributed sensor networks or enterprise environments. Select DAQ solutions supporting secure remote configuration, live data streaming, and firmware updates. Ensure robust authentication and access control to protect sensitive measurement data and system integrity.
Define whether your application demands only raw signal capture, or if integrated data processing, advanced analytics, and report generation are also needed. Some industries—such as automotive, aerospace, and pharmaceuticals—require in-line filtering, digital signal processing (DSP), or data fusion algorithms to meet regulatory and performance standards. Choose DAQ systems with built-in processors, field-programmable gate arrays (FPGA), or embedded analytics based on your needs.
Determine the number and type of input channels needed—their maximum sampling rates, synchronization precision, and compatibility with your sensor array. Multi-channel, simultaneous sampling DAQ systems are ideal for high-precision, time-correlated measurements in applications like vibration analysis, power quality monitoring, or multi-axis force testing.
When selecting a data acquisition system, consider your specific measurement requirements, environmental conditions, and integration needs with existing test, monitoring, or automation infrastructure. Evaluate system scalability, supported communication protocols, sampling resolution, and accuracy specifications. Engage with reputable DAQ manufacturers and solution providers for technical advice, demo opportunities, and after-sales support. Request application notes, case studies, or user success stories related to your industry to better benchmark system performance.
Budget is another key aspect—balance upfront hardware/software costs with long-term maintenance, upgrade, and calibration expenses.
This section will explore various types of data collection systems and the signals they use.
Different kinds of data collection systems are as follows:
Data logging involves recording information over a specified duration. Typically, data loggers are compact devices designed to measure relatively small signals. These systems are often used for long-term data collection.
Based on the application, the data can include readings of voltages, temperatures, humidity, currents, or other relevant signals. Data loggers are standalone systems equipped with integrated processors and pre-installed software.
A data logger can operate independently as a standalone unit. Its popularity stems from its portability and simplicity for specific tasks. Each data logger has built-in storage for data retention, with some models featuring SD (secure digital) card slots for expanded memory using memory cards. Data can be gathered and stored temporarily on the logger, then transferred through a data link (such as a removable memory card) at regular intervals. Additionally, some web-enabled data loggers can be set up to share data directly over a network.
Some data loggers are battery-powered for additional portability. By definition, a data logger will consist of a more limited set of inputs and tend to have a more basic format like the already mentioned signals including temperature, current, voltage, etc. Data loggers can also be used for the collection of geological data for long-term monitoring of many items.
Data acquisition devices feature signal conditioning circuitry and an analog-to-digital converter but must be linked to a computer to operate. They are favored for their versatility and broad applicability. These devices are more intricate than simple data loggers but less complex than comprehensive rack-based DAQ systems. Typically, data acquisition devices use single units to connect all sensors, forming a complete DAQ system.
Data acquisition devices offer greater functionality than data loggers while being more affordable than full rack-based systems. Many of these devices are USB-based, and some can be plugged in for data acquisition tasks. Users can opt for pre-configured data acquisition software like DAQami or utilize programming environments such as C++, MATLAB, Python, and DASYLab. These devices provide a flexible and customizable solution for specific needs, with various BUS options and the capability to integrate into larger data acquisition systems.
Modular data acquisition systems are tailored for applications with high channel counts and complex requirements, accommodating numerous input channels and various types of sensors that need to be integrated and synchronized. They are used in more challenging scenarios where complexity and flexibility are crucial. Although these systems are the most costly option, they offer unique features and capabilities, such as those found in PXI systems, that are essential for performing intricate functions.
These DAQ systems are capable of performing both static and dynamic measurements, allowing for both low-speed and high-speed sampling.
Typically, modular data acquisition systems are paired with a high-performance computer to meet their demanding requirements. This computer can be either integrated into the system or connected externally. While these systems provide robust and flexible performance, they also come with a higher price tag. Modular data acquisition systems can be found in larger rack configurations, though many compact versions are also available.
Examples of data acquisition techniques include:
Voltage signals are among the most frequently used with DAQ systems. Devices such as strain gauge bridge circuits, thermocouples, and gas concentration sensors generate voltage signals. The data acquisition hardware processes these signals and uses an analog-to-digital converter to transform them into digital values, which are then stored by the computer. DAQ systems are typically capable of directly handling low-voltage inputs ranging from a few millivolts to several volts.
Current is commonly employed for signal transmission in noisy environments due to its reduced sensitivity to background noise. The data acquisition system measures the current flow and records the value in a computer for further analysis.
Electrical power supply signals can be monitored by using a current-sensing resistor to detect current and resistive dividers to scale down high voltage signals. The data acquisition system measures and stores the data from the current-sensing resistor, enabling the monitoring of power signals.
Instead, the voltage produced by a thermocouple is influenced by the temperature difference between the junction where the two different metal wires meet and the temperature at the cold junction, where the wires end.
These sensors generate a low voltage signal, usually in the millivolt range. The correlation between this voltage and temperature is nonlinear.
The precision of a thermocouple depends on its specific type.
Resistance measurements are taken using a current source along with a standard voltage input. The current passes through an unknown resistance, and the resulting voltage drop across the resistance is measured. This voltage drop is then recorded by the data acquisition system.
Strain gauge bridges are a specialized method for resistance measurement. They function based on the principle that electrical conductance depends on the conductor's geometry. A Wheatstone bridge setup is used to measure the gauge's resistance, which changes as the gauge is deformed by applied strain. The system detects small variations between the two circuits of the Wheatstone bridge.
As a result, strain measurements often focus on detecting deviations from initial values rather than absolute measurements. Knowing these initial values is crucial, as they may be significantly larger than the changes caused by strain-induced bridge imbalances. To capture these small changes accurately, a high-resolution analog-to-digital converter is used to provide the necessary dynamic signal range.
Digital outputs from devices like switches are interpreted as logic signals, being detected as either on (1) or off (0). For volt-free contacts, where no external voltage is switched, a small sensing voltage, typically 5V to match TTL levels, is used to determine the switch state. When external voltage is switched, the logic state is identified by the voltage level itself, such as TTL, up to 12V logic, or 24V DC. For rapidly changing digital signals that form pulse trains, counter-timer inputs should be used.
This chapter will explore the uses and advantages of data acquisition systems.
These are:
Data acquisition systems are employed in the electronics industry to test various parameters involved in electronics design, such as heat generation, resistance, conductivity, and magnetic properties.
In automotive manufacturing, data acquisition devices are used to assess the quality of produced parts.
Data acquisition systems are employed to perform quality testing on imaging equipment, such as photographic lenses and video cameras, as well as on scientific instruments like scanners and microscopes.
In laser technology, data acquisition systems are used to evaluate laser performance, light intensity, and color.
In radar and sonar applications, data acquisition systems employ remote sensing technologies to assess their efficiency and performance.
Industrial machines are designed for repeated use, making repeatability crucial. Data acquisition systems are frequently used to test these machines for their tolerance to repetitive forces.
Data acquisition systems are used in non-destructive testing of structures, geological studies, seismology, ultrasonic measurements, and the analysis of acoustic emission phenomena.
Gas detectors are used to find leaks using tracer gases, such hydrogen and helium. Once a chamber is filled with the trace gas, the loss of gas is measured with a mass spectrometer that detects the trace gas. Gas detection is used to determine the amount and composition of the gas being lost from a system or machine.
Benefits of data acquisition systems include:
Data acquisition systems enhance the precision of measurements.
Data acquisition systems enable the creation of customized mixed-measurement systems to meet specific requirements.
Hardware options vary widely, from handling limited data to managing multiple data acquisition systems that work together in a synchronized setup.
Data acquisition systems are employed in critical facilities globally to monitor essential parameters. The data gathered is used to improve efficiency, ensure reliability, and maintain safe operation of machinery.
Measurements are displayed in real-time, allowing technicians to quickly address any issues and make necessary repairs to maintain optimal performance.
Advanced data acquisition systems help companies reduce data duplication and integrate technology, simplifying the analysis of collected data. These systems enable employees to work more efficiently without disruptions that could affect their productivity.
Data acquisition systems automate data entry tasks that were previously handled manually. This automation minimizes errors caused by human input and frees up staff time for other responsibilities.
Reducing the number of interfering programs makes the workflow more efficient. Data acquisition systems provide comprehensive information and ensure accuracy without relying on additional applications.
Data acquisition systems enable users to access and retrieve information from the database for processing and analysis.
A data acquisition system allows for the tracking and monitoring of a company’s processes, helping to identify and address issues more rapidly.
With data capture now automated and objective, human error is minimized, reducing security risks related to data storage, analysis, and management.
Faster data entry reduces memory usage and allows for immediate recovery of information through DAQ systems, making work processes more cost-effective.
Data acquisition systems ensure that a system adheres to design specifications, thereby meeting user requirements. These systems offer testing capabilities to verify product quality before production and provide tools for analyzing defective products.
Data acquisition systems are highly versatile, available as either multi-purpose devices with an all-in-one setup capable of measuring various properties or as single-purpose devices designed for measuring a specific property.
Data collected by a data acquisition system is stored on a computer, making it easy for users to analyze or process the information as needed. Additionally, transferring data to other devices is simplified through computer integration.
Data acquisition systems provide unbiased information that helps improve company performance and enhance economic outcomes.
These systems offer enhanced control over organizational processes and enable rapid responses to any issues. They contribute to increasing efficiency and elevating the quality of products and services.
Data acquisition systems are a process for capturing, storing, analyzing, and manipulating data. The data is acquired through different techniques including voltage signals, current signals, power signals, etc. There are different types of data acquisition systems utilized. Some of them are multipurpose devices with an all-in-one configuration whereas some are single-purpose devices designed for measuring data from single parameters. Data acquisition systems can be applied in a wide variety of industries including the automobile industry, the electronics industry, laser technology, etc. These systems offer so many benefits. They are cost-effective, fast, versatile, and reliable. Data acquisition systems are a very efficient and convenient way of recording data for further analysis. Data acquisition systems not only improve data security since the process of capturing data is now automated, they improve access to data for the users while reducing errors.
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