Smart Sensor for Mining Equipment
Researchers and students at the Institute of Physics and Technology of Petrozavodsk State University (PetrSU) have developed the WVS wireless sensor for predictive diagnostics and continuous condition monitoring of mining equipment.
Vibration analysis is a fundamental diagnostic method for rotating and impact machinery, capable of detecting more than 80 percent of equipment faults. In mining, this is critical: extreme operating loads can cause an ordinary jaw crusher to fail, bringing an entire crushed-stone production line to a standstill. Yet commercial monitoring systems are expensive, while low-cost alternatives merely transmit raw signals, placing unnecessary loads on networks and servers.
Edge Computing Instead of the Cloud
PetrSU developers have created an affordable alternative: the Wireless Vibration Sensor (WVS). It attaches to equipment housings using either a magnet or a threaded connection and processes signals directly on the device through edge computing, reducing the load on data transmission networks. The unit is built around a three-axis MEMS accelerometer with a frequency range of up to 6 kHz, a dual-core microcontroller with Wi-Fi connectivity, and up to five temperature sensors. A real-time operating system assigns separate tasks to the two processor cores: one continuously polls the sensors to prevent data loss, while the other handles computations and communications. The sampling rate is 13.3 kHz, and a complete processing cycle takes 0.66 seconds. The embedded software simultaneously calculates statistical parameters, performs spectral and modulation analysis, and monitors temperature conditions. The two-threshold alarm system complies with the international standard ISO 20816 and is designed to prevent false alarms.
Spectral Analysis for Quarry Equipment
One of the project's scientific achievements is a method for improving the accuracy of spectral estimates. The developers combined logarithmic-parabolic peak interpolation with complex-spectrum interpolation, reducing amplitude error from 37 percent to 6.1 percent. The algorithm's performance was confirmed through numerical simulations and tests on a vibration test bench.
A distinctive feature of the project is its focus on specialized equipment manufactured by Karelia-based Sverus LLC, including cone crushers, jaw crushers, and vibrating screens. The main technical challenge was the low-frequency range of the equipment's vibrations, down to 15 Hz, resulting from the large dimensions of the machines and their bearings. This required the developers to adapt Fourier transform algorithms, develop envelope spectra, and create new signal analysis modules. In this configuration, data are transmitted over Wi-Fi using the Modbus TCP protocol. This allows the sensor to be integrated into existing automated process control systems without additional gateways or cable runs.
From Grant Funding to Quarry Operations
The project, titled "Development of a Primary Vibration Diagnostics System for Stone-Crushing Equipment in the Mining Industry," won a grant competition organized by the Head of the Republic of Karelia. The system has already undergone testing at stone-processing facilities in the region. The data collected during these trials will serve as the basis for an expanded control system for a crushed-stone production line.
The WVS is part of PetrSU's broader portfolio of import-substitution technologies. Previously, the university's Engineering Center developed an integrated vibration monitoring system with cloud-based data processing. It was successfully deployed at manufacturing facilities, including Petrozavodskmash JSC. The new autonomous sensor builds on the earlier system's core principles, namely digitizing signals directly at the monitored equipment and enabling straightforward integration without modifying existing control systems. However, it moves the processing workload from the cloud onto the device itself. This makes predictive maintenance technology viable in demanding environments where cloud-based solutions are less effective, including remote mining sites, forestry and chemical facilities, and pulp and paper mills.
Investing in the Technologies of the Future
Russia has been actively developing an ecosystem for predictive diagnostics in recent years. In 2022, Ruselectronics JSC introduced its Vibrotex monitoring system. A year later, Petrozavodskmash JSC implemented monitoring across its machine tool fleet. In 2024, Norilsk Nickel PJSC launched an intelligent monitoring system at its Talnakh Concentrator. TIUS LLC and PLKSystems LLC also completed projects at Techno manufacturing plants using the LoRaWAN protocol.
As of the end of last year, Russia's predictive maintenance (PdM) market was estimated at $100 million. Implementing PdM enables industrial companies to reduce equipment downtime and improve production capacity utilization.
A New Era of Industrial Maintenance
PetrSU's development combines compact size, autonomous operation, and low development costs, creating conditions for wider adoption of predictive maintenance. These advantages could also open up markets in Russia, other CIS countries, and developing economies. Exporting the device, however, will require international certifications for quality and industrial safety. More broadly, affordable diagnostic tools of this kind give Russian manufacturers a way to compete effectively with foreign alternatives and strengthen the country's technological independence in industrial automation.
