Fault Diagnosis of High-Speed Train Bogie Based on the Improved-CEEMDAN and 1-D CNN Algorithms

Abstract

Realizing the accurate fault diagnosis of high-speed train (HST) bogie is of great significance for ensuring the safe operation of HSTs. This article proposes a novel fault diagnosis method to identify the fault states of HST bogie and localize the positions of faulty components simultaneously by virtue of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and 1-D convolutional neural network (1-D CNN). First, the raw vibration signal is decomposed into multiple intrinsic mode functions (IMFs) via the ICEEMDAN. Then, the high-frequency components of IMFs obtained by ICEEMDAN are selected as the fault features of the HST bogie. Afterward, the 1-D CNN model is adopted to learn the deeper features from the high-frequency components to conduct fault classification and localization. The prediction accuracy of the proposed method is 99.3% and 98.7% on two bogie data sets, respectively. Meanwhile, experimental results demonstrate the effectiveness of the proposed method in identifying both the categories and the locations of faulty components, whose superiority has been verified by the comprehensive comparison analysis with traditional deep learning methods and state-of-art methods, including residual-squeeze net, convolutional recurrent neural network, and DenseNet.