Data Acquisition System

A Data Acquisition System (DAQ or DAS) is a collection of hardware and software components designed to measure physical phenomena from the real world, convert the resulting analog signals into digital data, and process that information for analysis, storage, and visualization [7]. These systems form a critical bridge between the physical environment and the digital domain, interfacing with a wide array of sensors and transducers to collect real-world data [7]. Accurate data collection via DAQ systems is a fundamental step in the design, prototyping, and testing processes across numerous industries, making them an indispensable component of modern test and measurement environments [7]. They are broadly classified by their architecture, which can range from simple, portable devices to complex, distributed systems integrated into industrial networks. The core function of a DAQ system involves signal conditioning, analog-to-digital conversion (ADC), and data processing. Signal conditioning prepares often weak or noisy sensor outputs for accurate digitization; this includes amplification, filtering, and isolation, with many devices featuring onboard amplifiers for low-level analog signals [2]. The ADC process samples the conditioned analog signal at a specified rate, a critical step where improper configuration can lead to aliasing, a phenomenon where frequency signals outside the desired band erroneously appear within the bandwidth of interest due to the sampling process [3]. Key characteristics defining a system's capability include its sampling rate, resolution, channel count, and the types of input signals it can handle (e.g., voltage, current, digital I/O). Main types of DAQ systems range from benchtop instruments and modular chassis-based systems, such as those adhering to the PXI hardware specification [5], to compact, computer-connected USB devices and embedded controllers. Data acquisition systems have extensive applications and significant modern relevance, particularly in research, industrial automation, and product validation. They are vital in automotive and aerospace testing for tasks like structural analysis and engine monitoring [7], and in specialized fields like fuel cell testing, which demands high DC common-mode voltage rejection for accurate measurements [4]. The software controlling these systems, such as National Instruments LabVIEW, provides the graphical programming environment for configuring hardware, automating measurements, and analyzing data [8]. Their significance is further underscored by their role in safety-critical applications, where systems may need to comply with international functional safety standards like IEC 61508 [6]. From the foundational technology of the oscilloscope, an early form of data acquisition instrument [1], to today's sophisticated, software-driven systems, DAQs remain essential for converting empirical observations into actionable digital information, driving innovation and ensuring quality and safety in technological development.

Overview

A data-acquisition system (DAQ) is an integrated electronic system designed to measure, condition, digitize, and record physical phenomena from the real world for subsequent analysis, visualization, and storage [13]. These systems form a critical bridge between the analog physical environment and the digital computational domain, enabling precise measurement and interpretation of parameters such as temperature, pressure, voltage, current, strain, vibration, and sound [13]. DAQ systems are fundamental components in scientific research, industrial testing, and process monitoring, where accurate and reliable data collection is paramount for validation, control, and decision-making [13].

Core Components and Architecture

A typical data-acquisition system comprises several key hardware and software subsystems that work in concert. The primary hardware components include:

Sensors and Transducers: These devices convert a physical quantity (e.g., force, temperature) into an electrical signal. Common examples are thermocouples, resistance temperature detectors (RTDs), strain gauges, piezoelectric accelerometers, and LVDTs (Linear Variable Differential Transformers) [13].
Signal Conditioning Circuits: Raw signals from sensors often require processing to be suitable for measurement. Signal conditioning functions include:
- Amplification (e.g., using an instrumentation amplifier with a gain of 10 to 1000)
- Filtering (e.g., anti-aliasing low-pass filters with cutoff frequencies set below half the sampling rate)
- Isolation (using optical or magnetic isolation to protect the system from high voltages)
- Linearization (for sensors like thermocouples with non-linear output)
- Excitation (providing a constant current or voltage to active sensors like strain gauges) [13].
Analog-to-Digital Converter (ADC): This is the core component that digitizes the conditioned analog signal. Key ADC specifications include:
Resolution: The number of discrete digital values (codes) the ADC can produce, expressed in bits. A common resolution is 16 bits, providing 65,536 (2^16) possible values.
Sampling Rate: The speed at which the ADC converts the analog signal, measured in samples per second (S/s) or hertz (Hz). Rates can range from a few S/s for slow processes like temperature monitoring to several giga-samples per second (GS/s) for high-speed digitizers.
Input Range: The span of analog voltages the ADC can accept, such as ±10 V or 0-5 V.
Accuracy: The difference between the actual input voltage and the voltage represented by the digital code, often specified as a combination of offset error, gain error, and nonlinearity [13].
Data Acquisition Hardware Interface: This component houses the ADC and connects to the computer. It can be an internal card (PCI, PCIe), an external module connected via USB, Ethernet, or Thunderbolt, or a modular chassis system like PXI (PCI eXtensions for Instrumentation) [13].
Computer and Software: The computer runs specialized software to control the hardware, process the digitized data, and present it to the user. Software capabilities include device configuration, real-time display, data logging to disk, and advanced analysis [14].

System Function and Workflow

The operational workflow of a DAQ system follows a sequential process. First, a physical phenomenon is sensed and converted into a low-level electrical signal, often in the millivolt range [13]. This signal then passes through signal conditioning circuitry, where it is amplified, filtered, and otherwise prepared. For instance, a strain gauge in a Wheatstone bridge configuration might produce a signal that is amplified by a factor of 100 to utilize the ADC's full input range [13]. The conditioned analog signal is presented to the ADC. At precise intervals determined by the sampling clock, the ADC takes a "snapshot" of the instantaneous voltage and converts it into a binary number proportional to the amplitude [13]. According to the Nyquist-Shannon sampling theorem, to accurately reconstruct a signal, it must be sampled at a rate at least twice the highest frequency component present in the signal. For a vibration signal with frequency content up to 1 kHz, a minimum sampling rate of 2 kS/s is required, though practical systems often sample at 5-10 times the maximum frequency [13]. The stream of digital data is transferred to the host computer's memory via the hardware interface's bus. DAQ software, such as NI LabVIEW, provides a programming environment to configure these steps—setting the sampling rate, channel configuration, and trigger conditions—and to implement the data flow [14]. The software also handles tasks like scaling the raw ADC codes back into engineering units (e.g., volts, degrees Celsius, psi), displaying data in real-time on charts and graphs, and streaming data to storage media [14]. Advanced systems may incorporate real-time analysis, such as computing the Fast Fourier Transform (FFT) to convert a time-domain vibration signal into a frequency-domain spectrum for fault detection [13][14].

Applications and Importance

Data-acquisition systems are indispensable in a vast array of fields due to their role in converting empirical observations into quantifiable, analyzable information [13]. In automotive testing, DAQ systems are used extensively for engine performance mapping, crash test analysis (measuring deceleration with accelerometers sampled at 10 kS/s), emissions testing, and durability testing on test tracks, where hundreds of channels of data may be recorded simultaneously [13]. Aerospace testing relies on DAQs for wind tunnel experiments, structural health monitoring of airframes, and flight test instrumentation, capturing data on parameters like air pressure, temperature, and structural strain [13]. In industrial manufacturing and process control, DAQs monitor and log conditions on production lines (e.g., temperature in an oven, pressure in a hydraulic press) to ensure quality and optimize efficiency [13]. Scientific research in physics, chemistry, and biology uses high-precision DAQs for experiments ranging from measuring neuronal activity with microelectrode arrays to observing astronomical phenomena [13]. The accuracy and reliability of the data collected at this stage are foundational, as they directly influence design decisions, safety validations, scientific conclusions, and the fidelity of simulation models [13]. A well-designed DAQ system ensures data integrity, which is critical for the subsequent steps of analysis, visualization, and reporting that drive innovation and problem-solving across these disciplines [13][14].

History

The development of data acquisition systems (DAQs) is intrinsically linked to the evolution of measurement science and the increasing demand for converting real-world physical phenomena into quantifiable, analyzable digital data. This history spans from early analog measurement devices to the sophisticated, integrated digital systems of the present day, driven by advancements in electronics, computing, and semiconductor technology.

Early Foundations and Analog Predecessors (Pre-1970s)

The conceptual groundwork for data acquisition was laid by early electrical measurement instruments. The oscilloscope, a fundamental tool for visualizing electrical signals, represents a key precursor. The first commercially viable oscilloscope, the DuMont Type 241, was introduced in 1946, utilizing a cathode-ray tube for display. For decades, oscilloscopes and chart recorders were the primary means of capturing and visualizing transient electrical events, though data storage and automated analysis were severely limited. These were entirely analog systems, where the signal path from sensor to display involved minimal processing. Building on the concept of sensing physical phenomena discussed earlier, these early systems faced significant challenges with noise, bandwidth, and the manual interpretation of results. The need for more automated, accurate, and higher-channel-count measurement systems became apparent in industrial and scientific research, setting the stage for the emergence of dedicated DAQ hardware.

The Advent of Digital Instrumentation and Early DAQ (1970s-1980s)

The 1970s marked a transitional period with the introduction of instruments that incorporated digital logic for control and display, though the signal path often remained analog. A significant milestone was the 1982 introduction by Hewlett-Packard of the HP 1980A/B oscilloscopes, recognized as the first fully digital, microprocessor-based oscilloscopes. These instruments featured two 100 MHz channels and used analog-to-digital converters (ADCs) to digitize the input signal, enabling digital storage, processing, and display. This shift from analog to digital acquisition was a pivotal moment, demonstrating the practical benefits of digitization for measurement accuracy, repeatability, and data handling. Concurrently, the concept of programmable instrumentation began to take shape. The General Purpose Interface Bus (GPIB), standardized as IEEE-488 in 1975, allowed multiple instruments—like digital multimeters, power supplies, and oscilloscopes—to be controlled by a computer. This created the first widely adopted framework for automated test systems, a direct precursor to modular DAQ systems. In these setups, a computer orchestrated measurements from discrete instruments, aggregating the data. However, these systems were often large, expensive, and complex to configure, limiting their use to high-end laboratories. The true genesis of the modern, computer-centric DAQ system occurred in the mid-1970s and 1980s with the development of plug-in data acquisition boards for early microcomputers. Companies like Data Translation and National Instruments, founded in 1974 and 1976 respectively, pioneered this approach. National Instruments' first product in 1977 was the GPIB interface board for the Apple II, facilitating computer control of instruments. This was quickly followed by analog input/output boards that plugged directly into computer expansion buses. These boards integrated critical signal conditioning components—such as amplifiers, filters, and analog-to-digital converters—onto a single card, directly connecting sensors to the computational power of a desktop computer. This integration dramatically reduced system size, cost, and complexity compared to rack-and-stack GPIB systems.

Software Revolution and System Integration (1980s-1990s)

Hardware advances were paralleled by a software revolution that defined the usability and capability of DAQ systems. Prior to this, programming measurement routines required extensive knowledge of low-level languages and hardware registers. In 1986, National Instruments introduced LabVIEW, a graphical programming environment that used a dataflow paradigm with visual block diagrams instead of text-based code. LabVIEW and similar subsequent platforms abstracted hardware complexities, allowing engineers and scientists to focus on measurement logic, data processing, and visualization. This software layer became the "brain" of the DAQ system, enabling:

Intuitive configuration of sampling rates, gains, and triggers
Real-time data visualization and analysis
Automated logging of data to disk
Control of auxiliary hardware

This era also saw the refinement of key DAQ specifications. As noted earlier, sampling rate requirements are governed by the Nyquist-Shannon theorem. During the 1990s, ADC technology advanced rapidly, with resolution improving from 8-bit and 12-bit to 16-bit and 18-bit becoming common for precision measurements, while sampling rates pushed into the megahertz range. Furthermore, techniques to ensure signal integrity were refined. For instance, capacitive shielding, a method of mitigating electromagnetic interference by providing a low-impedance path for induced currents away from the signal lines, became a standard design consideration in high-quality DAQ hardware and cabling.

Proliferation, Standardization, and Connectivity (2000s-2010s)

The 2000s witnessed the proliferation of DAQ technology into a vast array of industries, from automotive and aerospace test stands to pharmaceutical manufacturing and environmental monitoring. The role of DAQ expanded beyond laboratory analysis to become integral to operational systems like Supervisory Control and Data Acquisition (SCADA), where it is used for real-time monitoring and control of industrial processes and infrastructure. SCADA data has since become a critical source for condition monitoring and fault detection in complex systems like wind farms and power grids [15]. This period was characterized by standardization and new form factors. The PCI eXtensions for Instrumentation (PXI) platform, introduced in 1997 and gaining widespread adoption in the 2000s, provided a robust, modular chassis-based standard for high-performance and synchronized multi-channel systems. Simultaneously, connectivity options exploded beyond GPIB and PCI slots to include:

USB: Offering plug-and-play convenience for portable and benchtop systems
Ethernet: Enabling distributed measurements over local networks
Wireless protocols: Allowing data acquisition from remote or rotating machinery

The performance of core components continued to climb. Semiconductor manufacturers like Texas Instruments focused on producing integrated data converters and signal chains with exceptional accuracy, low noise, and low power consumption, which were critical for demanding applications like bridge sensor and thermocouple measurements [13].

The Modern Era: Embedded, Intelligent, and Cloud-Connected (2020s-Present)

Today, data acquisition is evolving from a function performed by a dedicated box connected to a PC to an embedded capability within larger systems. The rise of the Internet of Things (IoT) and edge computing has led to the development of intelligent, networked sensors with built-in ADCs, microprocessors, and communication modules. These devices can perform local preprocessing, reducing data volume before transmission. Modern trends include:

High-Density Systems: DAQ devices with hundreds of channels in a single compact unit.
AI at the Edge: Incorporating machine learning algorithms directly into DAQ hardware for real-time anomaly detection and feature extraction.
Cloud Integration: Seamless streaming of acquired data to cloud platforms for large-scale analytics, long-term storage, and collaborative analysis.
Advanced Synchronization: Use of technologies like IEEE 1588 Precision Time Protocol (PTP) to synchronize data streams from devices distributed across wide geographical areas with microsecond accuracy. Furthermore, the principles of accurate data acquisition have become embedded in consumer technology, from the sensor arrays in smartphones to the diagnostic systems in modern vehicles. The ongoing miniaturization of electronics, improvements in wireless bandwidth, and advances in low-power design continue to push the boundaries of where and how data from the physical world can be captured, analyzed, and utilized.

Description

A Data Acquisition System (DAQ) is a comprehensive framework of hardware and software components designed to measure, digitize, and process electrical or physical phenomena—such as voltage, current, temperature, pressure, sound, or motion—into a form suitable for computer-based analysis, storage, and visualization [20]. These systems are fundamental to modern test, measurement, and monitoring applications across diverse industries, enabling the reliable collection of real-world data that informs design, prototyping, and validation processes [16]. Building on the foundational hardware components discussed earlier, a complete DAQ system integrates these elements with sophisticated software and processing units to form a cohesive measurement solution.

Core Function and System Architecture

The primary function of a DAQ system is to bridge the physical world and the digital domain. As noted earlier, the process begins with sensors converting a physical quantity into an electrical signal. The DAQ hardware, typically centered on an analog-to-digital converter (ADC), then conditions, digitizes, and routes this signal. However, the system's capability is fully realized through its controlling intelligence. This can be an external personal computer (PC) or, for higher performance and compactness, an embedded computer integrated directly within a modular chassis, such as in the PXI or PXI Express platforms, which eliminates the need for an external PC [17]. The software layer provides the user interface for system configuration, real-time data visualization, and post-processing, creating a closed loop from measurement to insight.

Key Performance Considerations and Design Challenges

Designing and implementing an effective DAQ system requires careful attention to several technical parameters that directly impact measurement fidelity.

Signal-to-Noise Ratio (SNR): SNR is a critical metric that compares the level of a desired signal to the level of background noise [19]. A higher SNR indicates a cleaner, more discernible signal. Noise can be introduced from electromagnetic interference, thermal effects in components, or improper grounding. Strategies to improve SNR include using high-quality, shielded cabling, employing differential measurement techniques, and optimizing sensor excitation levels. For instance, in strain gauge measurements, increasing the excitation voltage can improve the output signal level relative to fixed noise sources, thereby enhancing SNR, though this must be balanced against the risk of gauge overheating [18].
Sampling Rate and Bandwidth: The system's sampling rate, measured in samples per second (S/s), must satisfy the Nyquist-Shannon theorem, which states it must be at least twice the highest frequency component of the signal to avoid aliasing. As a previous section detailed, practical systems often sample at 5 to 10 times the maximum frequency of interest for better waveform representation. This requirement directly influences the choice of ADC and the overall signal-chain bandwidth.
Channel Density and Integration: Modern applications, such as multi-point vibration monitoring in industrial machinery, often require simultaneous measurement from dozens or hundreds of sensors [21]. This demands high channel density. Advances in integrated circuit technology allow for the consolidation of multiple signal-conditioning pathways and ADCs into single components, enabling smaller form factors and simpler system architectures [13].
Environmental and Application-Specific Factors: The design considerations are heavily influenced by the component being analyzed and the measurement techniques employed [21]. In harsh industrial environments, like those in process plants or power generation facilities, systems must be robust against temperature extremes, vibration, and corrosive atmospheres to ensure reliable long-term data acquisition for condition monitoring [16][22].

Applications Across Industries

Data acquisition systems are ubiquitous in engineering and scientific fields. Their application enables predictive maintenance, performance validation, and research discovery.

Industrial Condition Monitoring and Vibration Analysis (VA): One of the most prevalent uses is in monitoring the health of rotating machinery, such as turbines, pumps, and motors. VA, which relies on high-frequency data acquisition from accelerometers, has been applied for decades in industries including power generation, aerospace, and cement manufacturing to detect imbalances, misalignments, or bearing wear before catastrophic failure occurs [21][22]. This practice maintains operational efficiency and safety.
Product Testing and Validation: In automotive, aerospace, and consumer electronics, DAQ systems are integral to test benches and prototypes. They collect stress, thermal, and electrical performance data under simulated operating conditions, providing engineers with the empirical evidence needed to refine designs.
Scientific Research: From capturing physiological signals in biomedical studies to measuring atmospheric conditions in environmental science, DAQ systems provide the precise measurement capabilities required for experimental data collection.

Technological Evolution and System Design Resources

The evolution of DAQ has been closely tied to advancements in digital computing and modular electronics. Following the historical shift from analog oscilloscopes to digital, microprocessor-based instruments like the HP 1980A/B, the industry moved towards standardized, computer-controlled systems. The introduction of the GPIB interface, as mentioned previously, was a pivotal step in this automation. Today, designers leverage extensive resources to optimize system performance. These include selecting high-performance devices for specific signal-chain bandwidths, utilizing integrated components to increase channel density, and choosing low-noise power management devices to enhance overall system accuracy and stability [13]. This continuous advancement ensures that data acquisition systems remain capable of meeting the increasingly complex demands of modern measurement challenges.

Significance

Data acquisition systems (DAQs) represent a fundamental technological bridge between the physical and digital worlds, enabling the quantitative measurement and analysis of real-world phenomena across science, engineering, and industry [23]. Their significance extends far beyond simple measurement, forming the critical infrastructure for modern testing, monitoring, diagnostics, and control systems. The transition from analog instruments like oscilloscopes and chart recorders to integrated, computer-based DAQ platforms has revolutionized how data is captured, processed, and utilized, making high-fidelity measurement accessible and actionable.

Enabling Modern Test, Measurement, and Validation

DAQs are indispensable components in research, development, and validation environments, where they interface with a vast array of sensors to collect precise data for analysis [23]. This accurate data collection is a critical step in the design, prototyping, and testing processes for numerous industries, including automotive, aerospace, electronics, and consumer goods [23]. The capability to digitize analog signals allows for the captured data to be processed, manipulated, computed, transmitted, or stored with the full power of modern computing [3]. This digital workflow is essential for functional safety validation, where systems must be rigorously tested against defined safety integrity levels. This process combines analytical techniques, functional testing, fault simulation, and documentation to verify reliable operation under both normal and failure conditions, as outlined in standards like IEC 61508 [6]. The architecture of modern DAQ systems supports this demanding work. For instance, platforms like PXI (PCI eXtensions for Instrumentation) provide modular, high-performance chassis with robust controller options, such as the NI PXIe-8881 which offers processor configurations up to an 18-core Xeon, enabling complex real-time analysis and control [17]. This computational power, combined with precise measurement hardware, allows DAQs to move from passive data loggers to active components in automated test stands, control loops, and validation suites.

Advancements in Structural Health and Environmental Monitoring

A premier application demonstrating the transformative significance of DAQs is in Structural Health Monitoring (SHM), particularly for critical infrastructure like bridges and in harsh industrial environments. Specialized DAQ devices designed for such applications integrate high-performance analog-to-digital converters (ADCs) that deliver accurate measurements from sensors like strain gauges and thermocouples while maintaining low noise and low power consumption—a crucial factor for remote, long-term deployment [23]. The continuous application of SHM to the dynamic properties of historic structures in seismic zones can provide further attributes regarding the concept of a digital twin (DT) [14]. In this context, the DAQ system serves as the sensory nervous system, constantly feeding real-world data to calibrate and update numerical models over time and to evaluate structural capacity and damage after each significant seismic event [14]. This creates a living model of the structure, enabling predictive maintenance and informed safety decisions. Achieving this level of accuracy in field environments requires careful engineering to mitigate noise. For the low-level analog signals typical of bridge strain gauges or thermocouples, interference can be particularly troublesome when amplified by a DAQ device's instrumentation amplifier [4]. Effective strategies include:

Capacitive shielding: This technique works by bypassing or providing an alternative path for induced currents, preventing them from being carried in the signal wires [2].
Proper grounding and differential measurements: To reject common-mode noise.
Strategic filtering: Both hardware and software-based to isolate the signal of interest [4].

Critical Role in Medical Diagnostics and Life Sciences

In medical diagnostics, DAQ systems underpin essential life-saving equipment. The electrocardiogram (ECG) is a quintessential example, where a DAQ system captures the minute electrical potentials generated by cardiac activity. For comprehensive diagnostic applications, a 12-lead ECG is standard, requiring a multi-channel DAQ system capable of simultaneously sampling several bio-potential signals with high resolution and low noise to detect anomalies indicative of heart disease [23]. The requirements here are extreme: signals are often in the millivolt range, bandwidth is limited, and power-line interference (50/60 Hz) is a constant challenge. The DAQ must provide exceptional common-mode rejection and employ specialized filtering to extract the clinically relevant waveform. Beyond diagnostics, DAQ systems are equally vital in biomedical research, pharmaceutical testing, and patient monitoring systems in intensive care units, where they acquire data from a multitude of sensors tracking vital signs.

Foundation for Digital Transformation and the Industrial Internet of Things (IIoT)

The digitization capability of DAQ systems, as noted earlier in their core function, is the foundational step for broader digital transformation initiatives like the Industrial Internet of Things (IIoT) and Industry 4.0 [3][23]. By converting analog sensor data into a digital stream, DAQs enable:

Centralized data aggregation and analytics: Data from distributed sensors can be transmitted over networks to cloud or edge computing platforms for large-scale analysis.
Predictive maintenance: Machine learning algorithms analyze trends in vibration, temperature, and pressure data from DAQs to predict equipment failures before they occur.
Process optimization: Real-time data acquisition allows for closed-loop control systems that adjust industrial processes autonomously for maximum efficiency, quality, and safety. The evolution from standalone instruments to integrated systems is exemplified by the historical development of the oscilloscope. Building on the milestone of the first fully digital, microprocessor-based oscilloscopes introduced by Hewlett-Packard in 1982 (the HP 1980A/B with two 100 MHz channels), modern digitizers and DAQ devices have absorbed this functionality into more flexible, software-defined platforms [1]. While an oscilloscope remains a vital tool for visualizing electrical signals, a modern DAQ system can perform analogous sampling with ADCs, but with greater channel count, deeper memory, and tighter integration with processing software for automated analysis, aligning with the trajectory of automated test systems [1][17]. In summary, the significance of data acquisition systems lies in their role as the essential enabler of measurement-based science and engineering. They provide the critical link that transforms physical phenomena into actionable digital information, driving advancements in safety, healthcare, infrastructure resilience, and industrial efficiency. From validating the functional safety of an automotive braking system to monitoring the heartbeat of a patient or the structural integrity of a century-old bridge, DAQ systems are the unsung technological workhorses that make data-driven decision-making possible.

Applications and Uses

Data acquisition systems are deployed across a vast spectrum of scientific, industrial, and medical fields, each with distinct requirements for accuracy, channel count, sampling rate, and environmental robustness [23]. The effectiveness of any DAQ system is ultimately determined by its ability to meet these specific measurement requirements, which involves careful consideration of signal characteristics, environmental conditions, and the intended analysis [20]. From monitoring the structural integrity of bridges to diagnosing cardiac conditions, these systems form the critical link between physical phenomena and actionable digital data.

Structural Health and Condition Monitoring

A paramount application of DAQ is in Structural Health Monitoring (SHM), particularly for critical infrastructure like bridges and buildings in seismically active regions. Dedicated systems for this purpose utilize high-performance data converters to deliver accurate strain and vibration measurements with low noise and low power consumption, often from sensors like strain gauges and accelerometers permanently installed on the structure. For strain measurements, determining an optimal excitation voltage for the Wheatstone bridge configuration is crucial and is best established through experimental procedure to maximize the signal-to-noise ratio (SNR) [18]. The continuous data stream provided by SHM systems offers invaluable insights into the dynamic properties and long-term behavior of structures [16]. This data is fundamental for creating and calibrating numerical models over time, effectively contributing to the development of a structure's digital twin (DT). Furthermore, following seismic events, DAQ systems enable the rapid evaluation of a structure's residual capacity and the effectiveness of any retrofitting, as demonstrated in monitoring programs for historic buildings [16]. Condition Monitoring (CM) of rotating machinery, such as turbines, pumps, and motors, represents another critical industrial application, primarily aimed at predictive maintenance. Vibration Analysis (VA) is the most commonly employed technique in this domain, relying on DAQ systems to capture high-frequency vibration signals from accelerometers [22]. To ensure consistency and reliability in CM programs, standards like ISO 13373 provide detailed guidelines for the parameters to be acquired and analyzed at each phase of development [21]. These systems facilitate the early detection of faults like imbalance, misalignment, and bearing wear, preventing catastrophic failures and unplanned downtime. The technical challenge lies in acquiring data with sufficient fidelity—requiring high sampling rates and bandwidth—to resolve the often subtle frequency-domain signatures indicative of incipient faults [21][22].

Industrial Process and Environmental Sensing

In industrial process plants, DAQ systems are integral to operational control, safety, and reliability engineering. They acquire data from a diverse array of sensors measuring temperature (via thermocouples or RTDs), pressure, flow, and level. Systems designed for thermocouple measurements must accommodate low-millivolt signals and provide cold-junction compensation with high accuracy. However, acquiring reliability data in such environments is notoriously challenging, as even relaxed data quality requirements may rarely be satisfied due to harsh conditions, accessibility issues, and the complexity of operational contexts [16]. These systems must also often comply with stringent safety standards for use in hazardous areas. The acquired data is used for real-time process control, historical trending, and as a foundation for reliability evaluations that inform maintenance schedules and plant modification decisions [16]. Environmental and scientific research constitutes a broad category where DAQ systems record parameters like atmospheric pressure, humidity, radiation levels, and water quality metrics over extended periods. These applications frequently demand ruggedized, low-power systems capable of operating autonomously in remote locations. The key consideration is often maximizing battery life while maintaining sufficient measurement accuracy and data logging intervals to capture meaningful trends or events.

Medical Diagnostics and Biomedical Research

In the medical field, DAQ systems are essential for diagnostic equipment and biomedical research. A quintessential example is the electrocardiogram (ECG) machine. For comprehensive diagnostic applications, a 12-lead ECG system is used, requiring a multi-channel DAQ system capable of simultaneously amplifying, filtering, and digitizing the microvolt-level electrical potentials from the heart [23]. These systems must exhibit exceptionally high input impedance, excellent common-mode rejection to cancel electrical interference, and adhere to strict medical safety standards. Beyond diagnostics, similar DAQ principles are applied in research settings for electrophysiology, capturing neural signals, and monitoring other biopotentials, where the integrity of the low-amplitude signal is paramount.

Automotive and Aerospace Testing

The automotive and aerospace industries rely heavily on DAQ for research, development, and validation testing. Applications include:

Crash test analysis, using high-speed DAQ to record data from hundreds of sensors (accelerometers, strain gauges, load cells) in milliseconds. - Engine and powertrain testing, monitoring pressures, temperatures, rotational speeds, and torsional vibrations. - Fatigue testing, where DAQ systems record strain data over millions of cycles to validate component durability. - Flight test instrumentation, acquiring data on airframe stresses, control surface positions, and aerodynamic pressures. These applications often involve complex, channel-dense systems requiring synchronization across many measurements, sometimes in physically challenging environments with extreme temperatures and high vibration levels.

Key Engineering Considerations for Application-Specific Design

Selecting or designing a DAQ system for any application necessitates a rigorous analysis of requirements. As noted earlier, signals in the millivolt range and power-line interference are common challenges, particularly in biomedical and sensor-level industrial applications [23]. A fundamental engineering metric is the Signal-to-Noise Ratio (SNR), which quantifies the level of a desired signal relative to background noise; optimizing SNR is critical for making accurate measurements and can reduce the time engineers spend validating data or troubleshooting measurement issues [19]. Key factors to consider include:

Measurement Type: Determining whether the signals are analog voltages, currents, digital pulses, or direct sensor outputs like from a bridge or thermocouple.
Channel Count and Density: The number of simultaneous measurement points required.
Resolution and Accuracy: The smallest signal change the system can detect and how closely the digital reading reflects the true analog value.
Sampling Rate and Bandwidth: The speed at which the analog signal is digitized, which must exceed twice the highest frequency component of interest (Nyquist criterion) to avoid aliasing.
System Timing and Synchronization: The precision with which multiple channels are sampled relative to each other, crucial for multi-channel analysis like modal analysis or cross-correlation.
Environmental Robustness: The system's ability to operate reliably under specific temperature, humidity, and vibration conditions.
Software and Integration: The tools available for data visualization, analysis, and integration with control systems or enterprise databases [23][20]. By carefully balancing these factors against the specific demands of the application—whether monitoring a historic church for seismic damage, diagnosing a cardiac condition, or testing a new aircraft component—engineers can implement a DAQ system that effectively transforms physical phenomena into reliable, actionable data [20][16].