Wireless Vibration Sensor Technology: Benefits, Limitations, and Industrial Applications
Summary
This white paper provides an in-depth comparison of three primary methods for machine vibration monitoring: permanently installed wired data acquisition (DAQ) systems with IEPE accelerometers, handheld vibration analyzers, and modern wireless sensor networks. It evaluates each approach based on critical performance metrics such as measurement frequency range, amplitude accuracy, noise floor, and environmental suitability, as well as installation complexity and cost-effectiveness.
Through detailed analysis, the paper highlights that wireless vibration sensors, while not yet matching the full performance envelope of traditional IEPE systems, can now meet the requirements of 40% to 50% of typical industrial monitoring points. As wireless technologies continue to evolve, their share of practical applications is expected to expand, making them an increasingly viable component in a hybrid monitoring strategy.
Three Machine Vibration Monitoring Solutions
Permanently mounted vibration monitoring system: Conventional methods of machine vibration monitoring are based on a data acquisition system and vibration sensors. Data acquisition systems are either permanently mounted with wired connections or configured as work-around handheld vibration analyzers. Major vendors of wired permanently mounted systems include HBK, SKF, Emerson (CSI), Bently Nevada, Rockwell Automation, NI (National Instruments), B&K Vibro, Pruftechnik, Adash, Donghua, and more. These systems typically interface with IEPE types of accelerometers. They can also support connections to charge sensors, MEMs sensors, velocity, displacement, temperature, and strain gage sensors. The cost of such wired systems is often more than handheld vibration analyzers.
Handheld vibration analyzers: Crystal Instruments has developed and deployed work-around handheld vibration analyzers in the past 30 years with notable success by US-Navy. In 1996, shortly after Crystal Instruments introduced a PCMCIA-card based dynamic signal analyzer as its first product, a Seattle based company called DLI Engineering initiated a collaboration. DLI Engineering was the main provider of machine vibration monitoring equipment for the US-Navy at the time. The Crystal Instruments CI2250 data acquisition card can interface with a 3-axial IEPE accelerometer sensor, sample four input channels at 50 kHz, and conduct FFT spectral processing. DLI Engineering effectively integrated CI2250 technology into their hand-held computer DCX with ExpertAlert software.
Figure 1 and 2. CI2250 data acquisition card with handheld DCX computer
The U.S. Navy employs a robust vibration monitoring and maintenance program for submarines and aircraft carriers, leveraging Condition Based Maintenance (CBM+) and predictive analytics to ensure optimal performance and prevent unexpected failures. This program focuses on monitoring the condition of individual components and using historical data and statistical analysis to predict potential issues. Route-based acquisition techniques are developed to conduct periodic measurements to the machines in a predefined map. After the vibration data is collected and transmitted to the office, a rule-based expert system will be automatically applied to the massive amount of data. Machines with suspicious phenomena will be flagged and analyzed in more detail manually.
Starting from the 2010s, the US-Navy began adopting the CoCo-80 handheld vibration analyzer from Crystal Instruments into their predictive maintenance program. This handheld system was widely used in carriers and submarines across the US-Navy. It is much more powerful than its predecessor, DLI’s DCX. CoCo-80 is a compact, handheld signal analyzer designed for both field and laboratory use. It offers configurations with 2, 4, or 8 input channels, allowing for flexible data collection setups. The device is equipped with a 5.7-inch color LCD and is portably designed at a weight of less than 1.7 kg for field use. It operates on battery power, supporting over eight hours of continuous use. For data management, the CoCo-80 includes options for Ethernet, USB, and SD Card connectivity. The analyzer is capable of recording signals up to 102.4 kHz per channel and features comprehensive analysis functions suitable for a wide range of applications. All handheld vibration analyzers interface with IEPE types of accelerometers.
Figure 2. CoCo-80 from Crystal Instruments
Wireless Vibration Sensors: In the past 10 years, an increasing number of vendors have provided wireless-based sensor system solutions for machine vibration monitoring. Bently-Nevada, Fluke, LORD Wireless, NI, PCI, Sensoteq, SPM Airius, Yokogawa, and Emerson, to name a few.
The wireless sensor products from different vendors have different pro and cons. Crystal Instruments has now joined this vendor list and introduced the Ruby, a wireless vibration sensor. This white paper will compare the critical parameters between traditional solutions of using DAQ and external sensors with the wireless sensor solutions. We want to objectively analyze the advantages and disadvantages of wireless sensors so that this paper can serve as a guide to potential users who consider wireless sensors as part of their solutions.
Data Acquisition Systems and Wireless Vibration Sensors Comparison
How we compare
This document presents a comparative analysis between the Ruby wireless sensor developed by Crystal Instruments and the traditional vibration monitoring solution that uses a data acquisition (DAQ) system with IEPE accelerometers. At times, we also reference the specifications of other similar wireless sensors available on the market. The goal is to provide an objective evaluation of the advantages and limitations of wireless sensors, enabling potential users to make informed decisions when considering their adoption.
When compared to IEPE sensors alone, wireless vibration sensors may appear limited due to their integrated nature. A wireless sensor essentially functions as a compact data acquisition system that contains a sensing element, onboard storage, processing unit, wireless communication module, and battery all within its small enclosure. As a result, its size and reliability may not match those of standalone IEPE sensors. However, when viewed in the context of an entire system that includes the DAQ unit, signal cables, and IEPE sensors, the wireless solution offers significant benefits in size, simplicity, and reliability.
This comparison focuses on two distinct approaches to vibration monitoring: 1). Wireless sensors and their associated sensor networks and 2). Traditional DAQ systems paired with IEPE sensors.
The indictive plot below demonstrates why the sensor specifications of full sensor range, usable bandwidth, amplitude accuracy, sensor inherited noise, signal processing capability and environmental conditions are the most critical values to monitor.
Figure 3. Time capture, peak reading, and noise floor
This plot illustrates a typical vibration monitoring process in which a time-domain signal is transformed into an auto-power spectrum using signal processing algorithms. The peaks in the spectrum represent the vibration intensity at specific frequencies that are often related to machine rotational speeds, or RPMs. After a certain period—such as a few weeks—another spectrum is captured. Comparing the spectral peaks at the same frequencies allows users to track changes and perform fault diagnostics on the machine.
The sensor’s full-scale range plays a critical role in ensuring that the time-domain signals are captured without clipping or saturation. Additionally, the system’s signal processing capabilities—whether real-time or post-processed—must be evaluated, as accurate transformation from time to frequency domain is essential for reliable analysis.
The frequency response of the sensor determines the range of rotational speeds it can effectively measure. For example, a lower frequency limit of 0.5 Hz corresponds to 30 RPM, while a high-frequency limit of 5 kHz can capture phenomena occurring at up to 300,000 RPM. Within this frequency range, the amplitude accuracy must be clearly specified. Without this information, users cannot trust the validity of the spectral peaks, and faulty conclusions may be drawn from inaccurate or biased data.
The sensor’s inherent noise level must be at least an order of magnitude lower than the smallest vibration amplitude expected. Otherwise, there is a risk of analyzing noise or electrical interference rather than meaningful vibration signals.
Lastly, the environmental operating conditions—including temperature, shock, and humidity—must be clearly defined, as these factors directly impact the long-term reliability and performance of the sensor in real-world applications.
Now, let’s compare.
Single or three axes
It is well-known that the vibration sensors based on IEPE or charge methods can either be single axial or 3-axial. Many people choose 3-axis sensors for machine vibration measurements because with the same acquisition, 3-axis sensors will bring in more information about the machine’s condition.
Some wireless vibration sensors in the market only measure the single axis movement of the machine while some can measure the 3-axis. Ruby is a 3-axial vibration sensor.
Verdict: Both the IEPE accelerometer and wireless vibration sensors can provide either single- or 3-axis measurements.
Full scale range (g-level)
IEPE sensors have tremendous advantages over wireless vibration sensors in terms of their full-scale range. IEPE acceleration sensors from various vendors such as PCB 393B12 or Wilcoxon 731A for micro-vibration measurements can have a full range of as little as 0.001 g, or as high as 20,000 g from vendors such as PCB 350B04 or Dytran 3055B. The user has a wide range of choices to select from different g-levels of these IEPE sensors. Wireless vibration sensors, on the other hand, can rarely go below 1 g on the low limit due to their larger noise floor and rarely exceed 100 g due to the complexity of their internal electronics.
Most wireless vibration sensors in the market have measurement ranges between 10 g to 100 g.
For machine vibration measurement, the following table lists the most common vibration levels of machines:
| Application Type | Common Acceleration Range (Peak g) |
Notes |
|---|---|---|
| General rotating machinery (pumps, motors, fans) |
±5 g to ±10 g | This covers 90% of typical condition monitoring |
| High-speed spindles or turbines | ±20 g to ±50 g | Needed for high-frequency and high-acceleration events |
| Gearboxes and impact-prone systems | ±50 g to ±100 g | Captures transient events and mechanical shocks |
| Heavy industrial (e.g., crushers, mills) | ±100 g to ±500 g | For shock monitoring and failure prediction |
| Precision equipment (low-vibration) | ±0.5 g to ±2 g | For sensitive monitoring or micro-vibration |
This means that wireless vibration sensors with their current technology cannot be used for shock related high-g testing or micro-vibration measurements. They are adequate for vibration monitoring of general rotating machines, turbines, gear boxes and a variety of machines.
Verdict: With the available technology today, wireless vibration sensors may capture the market for a large range of general machine applications. However, their g-level ranges are very limited when compared with those of IEPE sensors.
Sensor bandwidth and measurement amplitude accuracy
Frequency range and amplitude accuracy are critical parameters in vibration measurement. IEPE and charge-type vibration sensors can reliably measure a wide frequency range from 0.1 Hz to 10 kHz, which is suitable for all types of machine vibration. At the low-frequency end, wireless vibration sensors can generally match the performance of IEPE and charge sensors, with some MEMS-based wireless models capable of detecting down to DC (constant acceleration).
However, wireless sensors face limitations at the high-frequency end. Because they incorporate complex internal electronics and are typically larger in size than IEPE or charge sensors, their upper frequency range is usually limited to around 2 kHz while maintaining acceptable accuracy.
Some vendors only provide the sampling rate of the ADC converters in their specifications instead of useful bandwidth. While the sampling rate has to be at least 2 times the bandwidth of interest and is a good indicator of the performance of the sensor, users care about the usable bandwidth claimed with certain amplitude accuracy. Only claiming the sampling rate of ADCs will confuse users.
A major drawback of many wireless sensor products on the market is the lack of disclosed amplitude accuracy in the frequency domain. These products are often shipped without calibration reports or frequency response function (FRF) curves, reflecting a lack of confidence in their performance. This absence of verified data remains one of the key reasons wireless sensors struggle to gain acceptance among serious users when compared to IEPE sensors.
Crystal Instruments' Ruby wireless sensor addresses this challenge by implementing a proprietary FRF compensation algorithm. With this technique, Ruby achieves a Z-axis measurement bandwidth of 0.5 Hz to 6.5 kHz with amplitude accuracy within ±1 dB to ±2 dB. The X and Y axes maintain bandwidths up to 4.5 kHz. This level of performance makes Ruby suitable for most machine vibration applications and puts its measurement accuracy on par with that of standard IEPE sensors.
Figure 4. A typical frequency response spectrum of Ruby sensor
Verdict: With the available technology today, wireless vibration sensors cannot compete with IEPE/Charge sensors in the high frequency range. By applying FRF compensation, wireless vibration sensors can meet the bandwidth requirement of a majority of machine vibrations.
Noise floor of sensors
Noise indicates how quiet the sensor measurement can be when no vibrations are present. Noise floor depicts the noise characteristics in the auto-power spectrum format, which is often represented in the spectrum unit of µg/√Hz. If the noise floor is too high, the real vibration signals to be measured will be submerged in the noise and the sensor cannot measure them. Wireless sensors, due to their low-power electronic design, are not as quiet as IEPE sensors. Here is a comparison:
| Parameter | Wireless Sensors in the market | IEPE Piezoelectric Sensors in the market |
|---|---|---|
| Noise Density | ~20–300 µg/√Hz | ~1–10 µg/√Hz |
| Effective Resolution | 100 µg to 1 mg RMS (wideband) | As low as ~10–50 µg RMS (wideband) |
| Low-Frequency Response (<10 Hz) | Poor to moderate (often noisier) | Excellent — used in seismic apps |
| High-Frequency Range | Usually <10 kHz | Easily >20–40 kHz |
| Use Case Fit | Broad machine trend monitoring | Fault diagnostics, early-stage detection |
Crystal Instruments’ Ruby has a noise floor as low as between 20–300 µg/√Hz, one of the lowest in the industry.
Figure 5. Noise floor of a typical sensor
Verdict: With the available technology today, the electrical noise of wireless vibration sensors is 1 to 2 magnitudes higher than that of IEPE sensors (which is bad). Wireless vibration sensors can meet the noise requirement of a majority of machine vibrations but not in the micro-vibration domain.
Signal processing capability
Various signal processing algorithms must be applied to the time domain signals that are acquired for machine vibration monitoring, which include digital filters, FFT, auto-power spectrum transform, cepstrum processing, envelope analysis, wavelet transform, and many others. The goal is two-folded: the first is to compress the time domain signals into the frequency domain and reduce storage; the second is to extract certain signatures such as amplitudes at certain rotating speeds and help determine machine conditions.
There are two different ways to weigh the capability of signal processing. One is referred to as real-time signal processing capability which means the capacity that time domain data is processed without gaps. The second is post-processing capability which only asks for time domain acquisition during the acquisition period.
No doubt that the traditional DAQ can have extremely powerful signal processing capabilities, either in real-time or post processing. The signal processing functions on wireless vibration sensors in the market vary from product to product. But the signal processing capability of wireless sensors are generally much weaker than those of traditional DAQ because these sensors can only use lower-power consumption parts.
Interestingly, if a wireless sensor can provide enough non-volatile memory to store the time domain capture, post-processing after these time domain captures are transferred to the workstation can be sufficient to meet the machine monitoring needs.
Verdict: Even the real-time signal processing capability of wireless vibration sensors usually are less powerful than those on a traditional DAQ, by combining time domain capture, limited on-board processing and stronger post-processing capabilities, wireless vibration sensors can meet the requirements of machine vibration monitoring.
Transferring data to workstations
The three common approaches to machine vibration monitoring are wired DAQ systems, handheld route-based analyzers, and wireless sensor networks, each with distinct strengths and limitations.
Wired DAQ systems, when paired with connected sensors, offer the most robust and reliable method for integrating with software platforms. They provide seamless, high-speed data transmission to the workstation, enabling real-time monitoring and analysis with minimal setup complexity once installed. Their high bandwidth and low latency make them ideal for applications that require high-fidelity and continuous data streams.
Handheld vibration analyzers, which depend on manual, route-based data collection, present the greatest challenges. Because these devices require an operator to walk to each measurement point, collect the data, and later transfer it to a central system, the process is time-consuming, labor-intensive, and prone to human error.
Wireless sensor networks represent a modern alternative. While their data transmission speeds are typically lower than those of wired DAQ systems due to bandwidth and power limitations, once properly deployed, they offer comparable convenience in terms of data access, automation, and integration with monitoring software.
Some wireless sensors in the market are designed so that their measurement schedule is independent of the schedule of data transmission, as depicted in the following diagram:
Figure 6. Timing of measurement, sleep, and transmission modes
This flexibility significantly enhances battery efficiency. For example, a wireless sensor can be configured to take weekly measurements and transmit data once a month. The devices remain in sleep mode most of the time to conserve energy.
Verdict: While wired DAQ systems offer the fastest and most reliable data transfer and are ideal for performance-critical applications, wireless sensor networks provide similar operational convenience and automation benefits, though at reduced transmission speeds. Handheld analyzers remain the most cumbersome and error-prone due to their reliance on manual intervention.
| Sensor Type / Application | Typical Operating Range | Example Models / Notes |
|---|---|---|
| Standard Industrial IEPE | –40 °C to +120 °C | PCB 603C01, Dytran 3055B, Wilcoxon 786 series |
| Extended High-Temp IEPE | –40 °C to +150 °C | PCB 357B03, Dytran 3225F |
| High-Temperature IEPE | –40 °C to +175 °C | Wilcoxon 797, Dytran 3305A |
| Ultra High-Temp IEPE | –55 °C to +200 °C | PCB 357A63, Dytran 3055E |
| Extreme / Specialty Sensors | Up to +260 °C or higher | Specialized models with radiation-hardened or shear-mode designs |
Wireless vibration sensors, due to their integrated electronics and power constraints, typically operate within a more limited temperature range. For example, the Crystal Instruments Ruby sensor is specified to operate within a temperature range of -40 °C to 55 °C.
In contrast, IEPE and charge-type accelerometers are capable of withstanding a much broader range of environmental extremes. General-purpose IEPE sensors commonly tolerate shock levels up to 1,000 g, while specially engineered high-shock IEPE sensors can withstand shocks as high as 50,000 g without failure.
The Crystal Instruments Ruby wireless sensor is rated to survive shocks up to 400g/1 ms, though its maximum measurable acceleration is 50 g. Additionally, Ruby can endure continuous 90 g sine vibration without failure, a key specification for qualifying its durability in environmental vibration tests. While its ±50 g full-scale range is sufficient for the majority of machine condition monitoring tasks, it will saturate under higher g-level inputs and may not survive extreme shock environments.
Verdict: IEPE and charge-mode accelerometers offer superior durability across temperature, shock, and high-vibration conditions. Wireless sensors, constrained by the complexity of their onboard electronics and battery systems, are suitable only within defined environmental limits. Therefore, users must thoroughly evaluate the operating conditions of their machines before selecting wireless sensor solutions.
Cost comparison
Given all technical advantages of performance, speed, and functionality of traditional data acquisition systems over wireless sensors, we still see a rising trend of adopting wireless sensors. One of the main reasons is cost. Wireless sensors in general cost much less than both permanent mounted wired data acquisition systems and workaround handheld data collection systems. The cost of using handheld data collectors not only comprises of the equipment and sensors, but also the human cost of more error prone operations.
The following table provides a comparison of monitoring equipment and sensors. Software cost is not included.
| Feature / Cost Factor | Wireless Sensor System | Conventional DAQ + IEPE Sensors | Handheld Data Collector |
|---|---|---|---|
| Hardware Cost per Machine Point | ~$1,000 | $1,500–$3,000 | $1,000–$2,000 (device shared; sensor ~$200) |
| – Sensor | N/A | $200–$1000 (IEPE) | $200–$1,000 (IEPE or MEMS) |
| – Gateway / Receiver | Shared ($100–$400 per point) | N/A | N/A |
| – DAQ System | N/A | $1,000–$2,000 | N/A |
| – Cabling + Installation | Minimal (battery-powered, no cable) | $200–$500 per sensor | Minimal |
| Operational Cost | |||
| – Installation Time | Low | High (cabling, mounting, routing) | Low |
| – Human Involvement | Low (fully automated monitoring) | High (setup, start/stop tests, data download) | Very High (manual walk-around and data logging) |
| – Maintenance | Battery replacement (~2–5 years) | Calibration, cable checks, amplifier upkeep | Calibration and frequent operator time |
| Scalability | High (easy to add more nodes) | Moderate to low (infrastructure expansion) | Low (limited to operator time and labor capacity) |
| Data Frequency / Bandwidth | Moderate (e.g., 1–5 kHz; periodic FFTs) | High (real-time, high bandwidth streaming) | High (real-time, high bandwidth streaming) |
| Synchronous Multi-channel Measurement | Not ideal (usually independent nodes) | Excellent (tight time synchronization) | Not possible |
| Analytics Readiness | High (edge processing, cloud-native) | High (manual or enterprise-level) | Low (manual review and trending) |
| Power Supply Dependency | Battery (or energy harvesting) | Wall/industrial power needed | Battery-powered handheld device |
This table shows that wireless vibration sensors have tremendous advantages compared to permanent mounted systems and route-based handheld collection approaches. The cost per machine point is 20% to 30% of that of traditional wired DAQ systems.
Conclusions
Based on the detailed analysis above, it is evident that wireless vibration sensors, while rapidly improving, still fall short of traditional IEPE or charge-mode sensors in several key performance categories. These include full-scale measurement range, usable frequency bandwidth, amplitude accuracy, inherent noise floor, signal processing capability, and resilience under harsh environmental conditions. In each of these areas, conventional sensor systems maintain a technological advantage.
However, it is equally clear that wireless sensor technology has advanced significantly in recent years. Modern wireless vibration sensors can now meet at least 50% of the requirements typically encountered in industrial machine condition monitoring. This level of performance is sufficient for many routine monitoring tasks where ultra-high resolution or precision is not critical.
One of the most compelling advantages of wireless systems is their cost-effectiveness. The total cost per measurement point for a wireless setup is typically only 20% to 30% of a conventional data acquisition system using wired IEPE or charge sensors. This substantial cost difference has a major impact on the increasing adoption of wireless solutions, especially in large-scale or distributed deployments.
For such applications, we strongly recommend a hybrid monitoring strategy by combining traditional DAQ systems for high-precision or diagnostic needs, handheld data collectors for flexible spot-checks, and wireless sensors for widespread continuous monitoring. When the performance specifications of wireless systems align with the measurement requirements, they should be prioritized as the first choice due to their lower cost, easier installation, and reduced operational overhead.
References
[1]. Product brochure, Ruby: Wireless sensor solution for vibration condition monitoring, Crystal Instruments Corporation, 2025