Fault analysis of High Speed Train with DBN hierarchical ensemble

Abstract

Deep Belief Network (DBN) learns the features of the raw data automatically, and develops a new idea for the study of fault analysis of High Speed Train (HST). Combining deep learning and classification ensemble technology, this paper presents a novel DBN hierarchical ensemble model for HST fault analysis. Firstly, Fast Fourier Transform (FFT) coefficients of the HST vibration signals are extracted as the state of the visible layer of the model, and then DBN is used to learn the hierarchical features of the vibration signals automatically. The features of each layer learned by DBN are used to train Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Radial Basis Function (RBF) Neural Network respectively. Finally, the Majority Voting (MV), the Classification Entropy Voting Principle (CE), and the Winner Takes All (WTA) ensemble strategies are used for combination to get the final results. The experiments are conducted by using laboratory data sets and simulation data sets. The results show that the fault recognition rate of the proposed model is much higher than the traditional fault analysis methods. In addition, unlike the DBN model, the proposed model is affected slightly by the number of network layers and the size of hidden units.