关键词: 钢轨打磨, 砂轮磨损, 神经网络, 磨损程度评价, 寿命预测

Abstract:

Rail grinding is a technology that removes the top fatigue layer of rails by applying high speed rotating grinding wheels. When the grinding wheels become abrasion, it typically leads to a decrease in material removal efficiency and an increase in grinding zone temperature. To prevent these issues from negatively affecting grinding operations, timely replacement of worn-out grinding wheels is necessary. This paper proposed a neural network-based method for predicting the wear level and residual life of rail grinding wheels, enabling the optimal timing for wheel replacement. The principle of this method is as follows: the axial acceleration signal of the motor spindle connected to the grinding wheel was collected, and based on this signal, characteristic parameters that describe the wear level of the grinding wheel were extracted. These characteristic parameters were then transformed using the Z-score method, which removes the dimensionality of the parameters and improves their comparability. After that, the XGBoost algorithm was employed to filter the most relevant features that are strongly correlated with the wheel’s service life. A fusion strategy integrating wear time and wheel wear volume was adopted as the criterion for assessing the wear and service life. Constructing a neural network model that mapped the filtered feature parameters to both the wear volume and the grinding wheel thickness. The experimental data along with the grinding wheels abrasion was obtained by using the experimental device. The data was divided into mutually independent training and validation sets, which were used to train and validate the constructed neural network, respectively. The results show that the method achieves comparable accuracy in both the training and validation sets. In the validation set, the accuracy for determining the wear level of the grinding wheel is 87.9%, with misclassified samples mainly concentrated in the transition zone of wear progression. The prediction accuracy for the grinding wheel life is -5.3%. Additionally, the method demonstrates a certain level of generalizability across different grinding process parameters.

Key words: rail grinding, grinding wheel wear, neural network, wear evaluation, residual life prediction