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 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. Results reveal a non-monotonic relationship between skidding ratio and axial load: skidding peaks with no load, over-skids at intermediate loads, and minimizes under heavy loads. Thermal imaging confirms the IC-TEA output negatively correlates with lubricant temperature (26.1% decrease for 9.2 °C rise), verifying its sensitivity to both skidding and temperature. A novel instability indicator quantifies significant cage stability deterioration during over-skidding. 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% in skidding and underestimate it by 33.37% during over-skidding. This integrated sensor-model framework provides unprecedented accuracy in predicting lubrication effects on bearing dynamics, enabling optimized skidding mitigation strategies for high-speed applications.