Integral-cage-based triboelectric assembly for accurate skidding and instability monitoring and fluid‒drag torque prediction of ball bearings

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.