Precise Diagnosis of Unknown Fault of High-Speed Train Bogie Using Novel FBM-Net
Yiming Zhang, Na Qin, Deqing Huang, Jiahao Du
Abstract
As the preferred mode of travel, the high-speed train (HST) and its healthy operation have received extensive attention. In the long-term service of HST, the track irregularity and wheel-rail wear may cause all kinds of faults towards the components of bogie, which is the only connection between the train body and the track. Taking into account the unknownness of bogie fault during actual operation, it is inappropriate to simply convert the issue of bogie fault diagnosis to the problem of group classification of known faults, as conducted in almost all reference works. In this paper, aiming at resolving the defect that supervised learning cannot identify unknown categories, the fractional Brownian motion (FBM) is integrated into an one-dimensional convolutional neural network (1D-CNN) to distinguish unknown bogie faults from known ones. The method takes the advantages of both 1D-CNN and FBM. On the one hand, the limitation in extracting feature artificially is broken by using convolution algorithm, and the deep features of original signals are extracted through stacking convolution kernels. On the other hand, different from one-class theory, e.g., one-class support vector machine (OCSVM), FBM brings randomness into the network to make the model sensitive to unknown faults. At last, four diagnosis strategies, i.e., SDE-Net, PSO-SVDD, CNN-LSTM-FCM, and OCSVM-ELM, are introduced to compare with the method proposed to verify the effectiveness and superiority. Experimental results reveal that the Fractional-Brownian-Motion-based Network (FBM-Net) can not only classify known faults efficiently, but also distinguish unknown faults with an accuracy of more than 93%.