A New Prediction Method for the Preload Drag Force of Linear Motion Rolling Bearing

Abstract

Existing studies focusing on the prediction of the preload drag force of linear motion rolling bearing (LMRB) are mainly based on mathematical modeling and vibration signal analysis. Very few studies have attempted to predict the preload drag force of LMRB on the basis of the raceway morphology. A 50 km running test was performed on a LMRB to study the correlation between the preload drag force of the LMRB and the change in raceway morphology. The preload drag force variation was measured in six regions using a surface profiler on a preload drag force test bench. The variational law for raceway morphology was characterized using the surface roughness Ra, maximum peak-to-valley height Rt, fractal dimension D, and recurrence rate Rr. The correlations between these four parameters (Ra, Rt, D, and Rr) and the preload drag force were 0.645, 0.657, 0.718, and 0.722, respectively, based on the gray correlation method. Hence, Rr is recognized as the optimal characterization parameter. Through the Gaussian process regression model, a preload drag force prediction model was established. Using the recurrence rate Rr as the input parameter to develop the prediction model, the accuracies of the prediction results of the three sets are 93.75%, 98.5% and 98.8%, respectively. These results provide a new method for the monitoring and prediction of the degradation of the preload drag force of a LMRB based on rolling track topography.

Keywords

• linear motion rolling bearing
• preload drag force prediction
• morphology characterization
• fractal theory
• recursive analysis