Sampling rate (frequency of measurement) is how often a system digitizes an analog signal per second. It’s vital for accurate data capture, analysis, and storage in aviation, audio, biomechanics, and industrial monitoring.
Sampling rate, also called sampling frequency, is a foundational concept in any measurement or data acquisition system. It refers to how many times per second a continuous-time (analog) signal is measured and converted into a digital value. This parameter, measured in hertz (Hz), defines how finely the system can resolve changes in the measured phenomenon over time. A higher sampling rate provides finer granularity, crucial for capturing rapid events, while a lower rate may suffice for slow or static signals.
Sampling rate is crucial because it determines how well a digital system can represent the original analog signal. In aviation, for example, black box recorders must sample fast enough to capture sudden control movements or transient vibrations. In biomechanics, force plates for jump analysis need high rates to detect brief, high-magnitude forces. In industrial monitoring, vibration sensors must capture high-frequency oscillations to detect early signs of machinery faults.
Too low a sampling rate leads to “undersampling,” missing critical events or distorting the signal—a phenomenon called aliasing. Excessively high rates, in contrast, burden storage and processing resources without improving useful information.
Sampling is a two-step process:
The time between samples is the sampling interval (inverse of the sampling rate). For example, a 1 kHz sampling rate means one sample every 1 millisecond.
Sampling rate is expressed in hertz (Hz), or samples per second. In some applications, kilohertz (kHz, thousands of samples per second) or megahertz (MHz, millions) are used.
Typical examples:
The Nyquist theorem is the mathematical foundation for sampling. It states:
To perfectly capture all information in a signal, the sampling rate must be at least twice the highest frequency present in the signal.
This threshold is called the Nyquist rate. If the signal contains frequencies up to 500 Hz, you must sample at least at 1000 Hz.
Aliasing occurs when a signal is sampled below the Nyquist rate. Higher-frequency content is “folded” into lower frequencies, distorting the digitized signal. In safety-critical systems, aliasing can hide or misrepresent important events.
Example:
If a 600 Hz vibration is sampled at 800 Hz, it appears as a 200 Hz vibration in the data—potentially masking a fault.
To prevent aliasing, analog anti-aliasing filters are used before the ADC. These filters block frequencies above half the sampling rate, ensuring only valid signal components are digitized. Since filters are not perfect, engineers often choose a sampling rate higher than twice the highest frequency of interest, allowing a “transition band” where the filter can roll off.
Sampling below the required rate causes:
Example:
Vibration in an aircraft engine at 800 Hz, sampled at 1 kHz, is at risk of aliasing if the anti-aliasing filter is not effective.
Sampling far above the needed rate:
Best practice: Sample at 2.5–10 times the highest frequency of interest, then down-sample or average if needed.
Every measured process has characteristic frequencies:
Tip: Review literature, perform spectral analysis (FFT), and consult manufacturer guidelines to choose the right rate.
| Application / Signal | Frequency Content | Suggested Sampling Rate |
|---|---|---|
| Human gait | <20 Hz | 50–100 Hz |
| Explosive sport/jump | up to 300 Hz | 500–1000 Hz |
| Audio (voice) | up to 8 kHz | 16–20 kHz |
| Audio (music/CD) | up to 20 kHz | 44.1 kHz |
| Power mains (50/60 Hz) | 50/60 Hz | 200–500 Hz |
| Vibration monitoring | up to 10 kHz | 25–30 kHz |
| Temperature/pressure | <1 Hz | 1–10 Hz |
Sampling rate is the backbone of digital measurement systems, dictating how accurately you can capture, analyze, and interpret dynamic phenomena. Whether you are designing an aircraft data acquisition system, configuring a biomechanics lab, or setting up industrial monitoring, understanding and applying the correct sampling rate is essential for reliable, actionable data.
For guidance on optimizing your measurement systems, or to discuss your specific needs, contact our experts or schedule a demo .
Sampling rate determines how often a system digitizes an analog signal. If set too low, rapid changes in the signal may be missed or misrepresented (aliasing), compromising the accuracy of data analysis. If set too high, it can lead to unnecessary data volume and processing load without improving meaningful fidelity. Choosing the correct sampling rate ensures accurate event capture while balancing storage and computational requirements.
The Nyquist-Shannon Sampling Theorem states that to accurately capture and reconstruct a signal, the sampling rate must be at least twice the highest frequency present in the signal. This threshold, called the Nyquist rate, prevents aliasing—where high-frequency information is misrepresented as lower frequencies in the sampled data.
If the sampling rate is too low, frequencies above half the sampling rate (Nyquist frequency) are 'aliased'—they appear as false, lower-frequency components in the data. This can hide critical events, distort analyses, and lead to incorrect conclusions, especially in safety-critical applications like aviation or machine monitoring.
Not necessarily. While higher rates can capture more detail, they also increase data storage and processing demands. Beyond a certain point, higher rates don't improve measurement quality and may even amplify noise. It's best to sample at a rate matched to the signal's frequency content, often 2.5–10 times the highest frequency of interest.
Anti-aliasing filters are analog low-pass filters placed before the analog-to-digital converter. They remove or attenuate frequencies above the Nyquist frequency, preventing high-frequency content from being misrepresented (aliased) in the digitized data. Effective filtering is essential for accurate digital measurements.
Ensure accurate data capture and analysis by choosing the right sampling rate for your application. Our experts can help you optimize your measurement strategy for aviation, industrial, or research needs.
Uncertainty in measurement defines the estimated range within which the true value of a quantity lies, accounting for all known sources of error. Proper uncerta...
Frequency is a foundational concept in physics and aviation, representing the number of cycles or events per unit time. It plays a critical role in radio commun...
Hertz (Hz) is the SI unit of frequency, defined as one cycle per second. Essential in aviation for radio communications, navigation, vibration analysis, and avi...
Cookie Consent
We use cookies to enhance your browsing experience and analyze our traffic. See our privacy policy.
