Selecting the right data acquisition (DAQ) system is essential for ensuring accurate, reliable results in test and measurement environments. Whether the application validates new designs, monitors system performance, or troubleshoots faults, the DAQ system must match the task’s specific technical and operational demands.
A new white paper, The Ultimate Guide to Data Acquisition for Test and Measurement, outlines the critical factors engineers and technical decision-makers should evaluate before committing to a DAQ solution. The paper also highlights the common pitfalls that can compromise data quality, operational efficiency, or long-term scalability.
Ensure the selection of a robust, future-ready DAQ system with these five key considerations, drawn from the white paper:
1. Understand the Measurement Requirements
The first step in selecting a DAQ system is clearly defining the required measurements. Temperature, pressure, voltage, current, and other physical parameters require appropriate sensor inputs and signal conditioning. Additionally, it is essential to assess the expected signal ranges and characteristics, as these will impact the necessary resolution, input types, and system design.
Matching the DAQ system’s capabilities to the intended application ensures compatibility with the sensors in use and helps avoid configuration errors that could lead to inaccurate or incomplete data.
2. Match the Sample Rate to the application
Sample rate is critical to capturing meaningful data, especially when monitoring fast-changing or high-frequency signals. Applications such as vibration testing or high-speed event recording require DAQ systems with high sample rates to avoid signal aliasing or data loss. In contrast, slower processes, such as temperature drift over time, may only require lower rates.
Selecting a system with appropriate sampling capabilities ensures that critical data is captured at the right fidelity, without overwhelming storage or processing resources.
3. Prioritize Accuracy and Resolution
Accuracy defines how close a measured value is to the true value, while resolution defines the smallest detectable change in a signal. Both are essential to producing high-quality data. For example, high resolution is critical in applications involving subtle signal variations or small-scale measurements.
The chosen DAQ system must offer specifications that meet or exceed the application’s accuracy and resolution requirements to ensure the validity of the test results.
4. Evaluate Connectivity and Software Compatibility
The system’s ability to interface with sensors, networks, and software platforms can significantly impact workflow efficiency. Ensuring that the DAQ hardware supports the necessary physical interfaces and protocols and that the software is compatible with the team’s preferred analysis, visualization, and reporting tools is vital.
Systems that integrate smoothly with existing infrastructure reduce setup time, streamline data handling, and improve overall productivity.
5. Avoid Common Pitfalls in System Selection
The white paper also identifies several frequent mistakes that can undermine DAQ performance:
- Neglecting signal conditioning: Without proper filtering, amplification, or isolation, signal integrity may be compromised.
- Overlooking scalability: A system that cannot expand to accommodate more channels or evolving needs may become obsolete too quickly.
- Underestimating software functionality: Limited or poorly integrated software can reduce the usability of even the most advanced hardware.
- Prioritizing short-term cost savings over quality: Lower-cost systems may lack the durability or precision required for critical testing applications, leading to rework or lost data.
Considering these five factors will help ensure a reliable, cost-effective solution that supports both immediate needs and long-term goals. For an in-depth look at system architecture, sampling techniques, and technical evaluation criteria, download The Ultimate Guide to Data Acquisition for Test and Measurement.
