System-level performance prognosis based on data augmentation for air brake systems of in-service trains

Abstract

In order to prognose the performance for air brake systems of in-service trains, a data augmentation method based on correlation analysis and improved multivariate support vector regression was proposed in this paper. By using black box theory and correlation analysis, brake cylinder pressure was extracted as the performance identification signal of air brake system, and the input and output signals were used as the auxiliary signals to construct the black box model of air brake system. Meanwhile, in order to make full use of the obtained data information, a data augmentation prognosis model based on improved multivariate support vector regression was established. Moreover, the optimal parameters of prognosis model were selected by means of particle swarm optimization algorithm, and RMSE and MAE were used as evaluation indicators. Finally, case studies on test-rig performance experiment data of air brake system were conducted. The prognosis model was trained and verified by using the service 1-7N and emergency, service full brake-relief and stage brake-relief experiment data, respectively, and the modeling and calibration error at pressure-holding stage were both in the order of ±5 kPa, which demonstrated the proposed method to be quite accurate and effective.