Su, L, Ma, L, Qin, N et al. (2 more authors) (2019) Fault Diagnosis of High-Speed Train Bogie by Residual-Squeeze Net. IEEE Transactions on Industrial Informatics, 15 (7). pp. 3856-3863. ISSN 1551-3203
Abstract
Fault diagnosis of high-speed train (HST) bogie is essential in guaranteeing the normal daily operation of an HST. In prior works, feature extraction from multisensor vibration signals mainly relies on signal processing methods, which is independent of the classification process. Based on convolutional neural networks (CNNs), this paper presents a novel fault diagnosis system using the residual-squeeze net (RSNet), which is directly applicable to raw data (time sequences) and does not require any signal transformation or postprocessing. In this network, information fusion is achieved by using the convolutional layer. More specifically, via the squeeze operation, an optimal combination of channels is learnt by training the network. Experimental results obtained by using SIMPACK simulation data demonstrate the effectiveness of the proposed approach in both complete failure case and single failure case, with diagnosis accuracy near 100%. The proposed approach also shows good performance in identifying the locations of faulty components. Comparisons between RSNet and competitive methods shows the advantages of RSNet for fault classification.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Convolutional neural network (CNN); fault diagnosis; high-speed train (HST) bogie; residual; squeeze |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jul 2019 14:06 |
Last Modified: | 24 Jul 2019 14:06 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TII.2019.2907373 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148907 |