Ruby Wireless Sensor

Reliable Data Transfer

The ISA-100.11a protocol is built on a mesh network, where small wireless nodes, known as motes, act as both sensors and data relays. These motes form a self-organizing, multi-hop network, ensuring data is transmitted reliably, even in challenging industrial environments.

100’s of Wireless Sensors

In a common machine factory, a few hundred wireless sensors can be installed on different machines. Multiple access points, i.e., gateways, will be installed at optimum locations to cover all the wireless sensors in the area.

2-Year Battery Life

The wireless sensors will usually be in the deep sleep mode. A wireless sensor will consume current at tens of µA level during deep sleep mode. This low-power consumption design allows the sensor to operate using a standard D-type battery for approximately 2 years.

Three hardware platforms:

• CoCo-70X - waterproof & dustproof handheld vibration analyzer

• Spider front-ends - permanently mounted systems equipped with patented high precision data acquisition technology

• Ruby - waterproof & dustproof wireless sensors

Machine monitoring software:

• RCM (remote condition monitoring) software is in charge of configuring the Ruby and Spider systems and acquiring data.

• VDS (vibration diagnostic system) software processes data using artificial intelligence and a rule-based expert system.

High Precision Measurement in 3-Axis

Ruby provides measurements in 3-axis as accurate as common charge vibration or piezoelectric sensor measurements. The frequency range goes up to 6.5 kHz while the tolerance is within +/- 1.0 dB.

One of Ruby’s key advantages is its ability to take measurements and send wireless transmission on independent schedules. This flexibility significantly enhances battery efficiency. For example, Ruby devices 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, as illustrated in the above diagram.

Minimize EMI

Some applications restrict the total EMI (electromagnetic interference) generated by sensors and their network. Sensors can be awakened in batches to minimize their EMI impact and placed into transmission mode at different time slots.

Industrial Wireless Networking

ISA-100.11a is an industrial wireless networking standard designed to support reliable and secure communication in industrial automation and control systems. It is particularly popular for wireless sensor networks, offering a self-organizing, multi-hop mesh architecture that enhances reliability, scalability, and real-time data transfer.

Remote Data Recording & Signal Processing

An onboard processor can realize the digital filtering, triggering, data window, FFT, auto-power spectrum and averaging operation. Demodulation spectral analysis is also available. Users can save calculated spectra along with recorded time waveforms into flash memory. These data recording and signal processing tasks can be scheduled through RCM, a PC-based Remote Condition Monitoring software.

Sampling Rates & Data Points

The highest sampling rate of ADC can go up to 16 kHz, which results in an excellent spectrum up to 6.5 kHz. The maximum data points that can be recorded are 62 million. The largest FFT size is 4096, which is sufficient for most of the machine vibration purposes.

Industry Leading High-Fidelity Device

Ruby is included amongst measurement devices with the highest fidelity offered in this industry. The noise floor of the sensor acceleration measurement is less than 100 μg /√Hz. The Z-axis vibration measurement can go up to 6.5 kHz while the X/Y horizontal axis can go approximately 4 kHz. The tolerance of measurement is typically less than +/- 1.0 dB.

Wireless Vibration Sensor Technology: Benefits, Limitations, and Industrial Applications

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.