Slipping Trend Prediction Based on Improved Informer

During locomotive operation, large amounts of operation data are recorded by the TCU (Traction Control Unit). The prediction and detection of slipping through the analysis of large amounts of data are of great significance for energy saving and locomotive operation safety. The TCU records time series data with a step length of 1 s. The transformer-based Informer algorithm performs well in time series prediction and analysis. Based on the improved Informer algorithm, this paper proposes a slip trend prediction method, which can predict the slipping state of n time steps according to the data of the previous seconds. By adding the improved prediction model of Informer to the classification model, this study, rather than adding a classification branch to the prediction model, directly improves the output structure, so as to realize long-sequence prediction with a multi-classification model. The model can effectively extract the important features in the data, and can realize multi-axle synchronous prediction and output the slipping state in parallel over the next few seconds. The comprehensive accuracy of this model in multi-axle synchronous prediction tasks can reach 94.75%. Finally, the model is analyzed according to the predicted results, and the effects of different models are compared. The attention mechanism and experimental data are analyzed by visualization.