@article{Gao2026, 
author = {Shuai Gao and Jintao Xiao and Song Wang and Jian Hu and Shuai Li and Huayan Pu and Jun Luo and Qinkai Han},
title = {Integral-cage-based triboelectric assembly for accurate skidding and instability monitoring and fluid‒drag torque prediction of ball bearings},
year = {2026},
journal = {Friction},
volume = {14},
number = {8},
pages = {9441183},
keywords = {rolling bearing, thermal effect, triboelectric, skidding, fluent model},
url = {https://www.sciopen.com/article/10.26599/FRICT.2025.9441183},
doi = {10.26599/FRICT.2025.9441183},
abstract = {Accurate monitoring of cage motion and skidding behavior is critical for ensuring the reliability of ball bearings in high-speed applications. However, existing methods are hindered by structural constraints and limitations in fluid drag modeling. This study proposes an integral cage-based triboelectric assembly (IC-TEA) for real-time, high-precision monitoring of the cage skidding ratio, rotational stability, and qualitative bearing temperature rise. Experimental tests show that IC-TEA quantitatively characterizes transient cage speed fluctuations and dynamics under varying loads, rotational speeds, and oil pressures. The results reveal a nonmonotonic relationship between the skidding ratio and axial load: skidding peaks with no load, overskids at intermediate loads, and minimizes under heavy loads. Thermal imaging confirmed that the IC-TEA output was negatively correlated with the lubricant temperature (26.1% decrease for a 9.2 °C rise), verifying its sensitivity to both skidding and temperature. A novel instability indicator is used to quantify significant deterioration in cage stability during overskidding. Leveraging IC-TEA kinematics as boundary conditions, a fluent-based computational fluid dynamics (CFD) model predicts lubrication states and fluid drag torque. This model reveals that traditional theoretical cage speed inputs overestimate drag torque by 33.75% during skidding and underestimate it by 33.37% during overskidding. This integrated sensor-model framework provides unprecedented accuracy in predicting lubrication effects on bearing dynamics, enabling optimized skidding mitigation strategies for high-speed applications.}
}