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

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 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.