Performance Degradation Prognosis Based on Relative Characteristic and Long Short-Term Memory Network for Components of Brake Systems of in-Service Trains

Abstract

During the service life of brake systems, performance degradation of the components is inevitable. In order to grasp the health status of components of brake systems, and aiming at the problem that the performance degradation trend of the components of the brake system is not completely clear due to signal coupling between components, the influence of variable working conditions, and the long performance degradation cycle, a performance degradation prognosis method of the components of the brake system based on relative characteristic (RC) and the long short-term memory (LSTM) network was proposed. The input and output signals of the components were isolated and fused, the working condition-independent RC was extracted to construct the health indicator (HI), and the validity of the HI was tested by using the monotonicity, correlation, and robustness metrics. Moreover, considering the time memory characteristics, the trend prediction of the HI curve of the components of the brake system was carried out based on the LSTM network. Furthermore, data augmentation for the training and testing sets was performed. Taking the typical component of brake systems as an example, a performance degradation test was carried out. The analysis results of the test data show that the accuracy of the performance degradation prognosis of the intake filter was over 99%, which validates the effectiveness and accuracy of the proposed method. The research results could provide a reference for health management and to improve the active safety protection capability of brake systems of in-service trains.