A new self-powered bearing sensor system tracks speed and skidding in real time, offering precise, durable monitoring to improve industrial machine reliability.
(Nanowerk Spotlight) Failures in high-speed bearings are a major cause of mechanical breakdowns across industries such as energy, transportation, and aerospace. Bearings are key to reducing friction between moving parts, but they often suffer from wear mechanisms like skidding and slippage. Once these failures begin, they can escalate quickly, leading to costly downtime. Current bearing monitoring methods—such as eddy current sensors, ultrasonic testing, and magnetic detection—face limits in accuracy, durability, and ease of installation. Many cannot operate reliably inside the tight spaces of real-world machinery, and most struggle to detect subtle early-stage damage.
Triboelectric nanogenerators (TENGs) have been proposed as an alternative. These devices generate electricity from mechanical motion using the contact electrification effect, and researchers have adapted them into self-powered sensors for monitoring vibrations, motion, and flow. However, earlier TENG-based bearing monitors usually tracked only one parameter, like rotation speed or cage motion, and often lacked the precision or durability needed for industrial use. Problems like rapid material wear or weak signals limited their usefulness, especially under the harsh conditions seen in high-speed machines.
Recent advances in material engineering and sensor design have made new approaches possible. Researchers from the Harbin Institute of Technology and the Beijing Institute of Nanoenergy and Nanosystems have now introduced a bearing condition monitoring sensor (BCMS) that combines compact size, self-powered operation, and dual-parameter monitoring. Their work, published in Advanced Functional Materials (“Synchronous In-Situ Self-Powered Monitoring Method for Speed and Skidding of High-Speed Bearing Based on Triboelectric Nanogenerator”), integrates two dynamic contact-mode TENGs directly inside the bearing. This allows simultaneous real-time tracking of bearing speed and skidding without the need for external power supplies or bulky sensors.
The BCMS uses two modules: one mounted on the bearing’s inner ring to measure rotation speed, and another attached to the bearing cage to detect slippage. A snap-fit design keeps both modules stable without disrupting bearing motion. By comparing the data from both modules, the system can detect when rolling elements begin to slip instead of rolling—a key sign of lubrication failure or excessive load.
A central feature of the system is its use of Dynamic Contact-mode Triboelectric Nanogenerators (DC-TENGs). In a DC-TENG, the rotor and stator surfaces lightly touch each other intermittently during operation. This contact is enough to generate strong electrical signals but limited enough to avoid rapid wear. This design achieves a balance that earlier contact-mode and non-contact-mode TENGs could not: it boosts the signal strength while maintaining long-term durability. Laboratory tests showed that the BCMS can operate for over 8 million cycles without losing accuracy.
Structural composition and working principle of the BCMS. a) Structural composition, b) The stator and rotor, c) Working principle of DCTENG. (Image: Reprinted with permission by Wiley-VCH Verlag) (click on image to enlarge)
The system measures bearing speeds between 600 and 6000 revolutions per minute (rpm) with a margin of error under 0.10%. Skidding rate measurements are accurate within 0.25%. To validate performance, the researchers compared the BCMS measurements with a commercial laser speed sensor. The results showed close agreement, confirming that the BCMS can reliably detect both rotational speed and skidding even as conditions vary.
The physical design of the sensor uses simple, efficient materials. The rotor electrodes are made of fan-shaped copper patches, while the stator has interlocked comb-shaped electrodes. The two surfaces are coated with materials that differ in their ability to attract or repel electric charge—specifically fluorinated ethylene propylene (FEP) and nylon. This enhances the generation of electrical signals during motion. A small 0.4 mm gap between rotor and stator balances strong signal output with low friction, preserving the bearing’s lifespan.
By analyzing the frequency of the electrical signals generated, the BCMS can track how fast the bearing rotates and how much the cage lags behind. Skidding is identified when the cage rotates slower than expected based on the bearing’s inner ring speed. The researchers also quantified the skidding rate—the percentage by which the cage falls behind—and showed that skidding increases with higher rotation speeds and lower radial loads. This behavior matches what is known about skidding physics: at high speeds, centrifugal forces reduce the traction between rolling elements and the raceway, making slippage more likely if loads are low.
The BCMS was further tested across different temperatures, humidity levels, and working conditions, confirming its ability to operate stably in a variety of environments. A real-time monitoring system built around the BCMS showed that the sensor can continuously track bearing health during speed changes, load shifts, and startup phases. It also demonstrated that higher startup speeds lead to more severe skidding, a key finding for designing safer startup protocols in machinery.
The researchers note that the BCMS is not limited to cylindrical roller bearings. With minor adjustments to the sensor size and mounting, the same principle can be applied to angular contact ball bearings, thrust bearings, and other bearing types used across mechanical systems. This adaptability suggests that self-powered, compact sensors like the BCMS could form the basis for smarter, more reliable mechanical equipment where real-time fault detection becomes standard.
This study shows a practical path forward for integrating TENG-based sensors into high-speed industrial systems. By solving issues of durability, compactness, and precision, the BCMS approach provides a solid foundation for future work on autonomous mechanical health monitoring.
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