How to Choose Sampling Rate

Sensing sampling rate is one of the most critical yet easily misunderstood parameters in dynamic measurement systems. Many people simply regard it as "how many data points are collected per second," but in fact, the choice of sampling rate directly determines whether we can truly reconstruct the dynamic process of a test and to what extent we can capture valuable information. Understanding the essence of sampling rate requires systematic consideration from three dimensions: theoretical foundation, engineering practice, and application requirements.

1. Theoretical Foundation

Sampling rate, simply put, is the number of times a device collects data per second, measured in Hertz (Hz). If we compare sensing testing to shooting a video: sampling rate = frame rate, each sample = one frame, and the changing process of the measured signal = continuous action. The sampling rate determines how fast a change you can capture. If the change is too fast and you sample too slowly, you will lose critical information.

Theoretical foundation: Nyquist sampling theorem. To reconstruct a signal without distortion, the sampling frequency must be at least twice the highest frequency component in the signal.

fs = 2 × fmax

Where fs is your device’s sampling rate, and fmax is the highest vibration frequency in the measured signal. If the sampling rate is insufficient, high-frequency signals will alias with low-frequency signals, causing complete and unrecoverable data distortion. An intuitive analogy: signal frequency is like the frequency of a person's voice. At 2× sampling, the result is like telephone-quality audio (understandable but not high-fidelity). At 5–10× sampling, the result is like CD-quality audio (high-fidelity reproduction).

2. Engineering Practice

The following table summarizes sensing sampling rates and recommended sampling rates for various applications for reference.

Table 1. Summary of sensing sampling rate requirements for different application scenarios

Application Category	Specific Application Scenario	Signal Characteristics & Frequency Range	Theoretical Minimum Sampling Rate	Recommended Engineering Sampling Rate
Static / Quasi-static monitoring	Building settlement monitoring	Very slow change, frequency < 0.01 Hz	0.02 Hz	0.1 – 1 Hz
	Landslide displacement monitoring	Slow creep, 0 – 0.001 Hz	0.002 Hz	0.01 – 0.1 Hz
	Concrete shrinkage monitoring	Daily/monthly changes, 0 – 0.0001 Hz	0.0002 Hz	0.001 – 0.01 Hz
Structural health monitoring	Bridge health monitoring	Low-frequency vibration, 0.1 – 10 Hz	20 Hz	50 – 100 Hz
	High-rise building wind-induced response	0.1 – 1 Hz	2 Hz	5 – 10 Hz
	Tunnel structure monitoring	0.1 – 20 Hz	40 Hz	100 – 200 Hz
Environmental & geological	Seismic intensity monitoring	0.1 – 50 Hz	100 Hz	250 – 500 Hz
Industrial processes	Large rotating equipment foundation vibration	0.5 – 30 Hz	60 Hz	150 – 300 Hz
	Pipeline monitoring	0.1 – 20 Hz	40 Hz	100 – 200 Hz
	Tank liquid level fluctuation	0.01 – 5 Hz	10 Hz	25 – 50 Hz
Transportation	Highway traffic vibration	1 – 30 Hz	60 Hz	150 – 300 Hz
	Ship motion monitoring	0.01 – 2 Hz	4 Hz	10 – 20 Hz

3. Application Requirements

In actual monitoring processes, the theoretical minimum sampling rate is only the starting point, not the end. Engineering practice tells us that merely satisfying the 2× relationship is far from sufficient. Real-world signals often contain noise, harmonics, and transient components, and systems have non-ideal characteristics. Experienced engineers typically apply a safety factor of 5–10×. For example, when monitoring bridge vibration with a highest natural frequency of 10 Hz, the sampling rate should be at least 50–100 Hz. This redundancy ensures accurate capture of the fundamental frequency.

Of course, there is a common misconception: "the higher the sampling rate, the better." This view ignores the practical burdens of high sampling rates. Excessively high sampling rates not only generate massive amounts of redundant data, increasing storage and processing pressure, but may also introduce high-frequency noise into the system, actually reducing the quality of the effective signal and lowering test efficiency. Therefore, the sampling rate should be chosen based on the actual signal characteristics and requirements, following the Nyquist sampling theorem as a foundation.

FiberLinkSource can accurately match customers with the best solution. Customers only need to tell us your application scenario and testing requirements, and we can provide constructive guidance based on actual engineering environments.

Correctly understanding and selecting the sampling rate is the foundation for ensuring the validity of dynamic measurement data and the reliability of engineering applications. Based on satisfying the Nyquist theorem, one must fully consider actual signal complexity, noise interference, and safety redundancy, choose the sampling rate configuration reasonably according to device specifications, and verify its suitability through actual testing – thereby ensuring complete and accurate dynamic measurement data that effectively supports subsequent analysis and applications.