Fault diagnosis of high-speed train bogie by residual-squeeze net
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.
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- Authors
- Su, L., Ma, L., Qin, N., Huang, D., & Kemp, A. H.
- Affiliations
- Southwest Jiaotong University (CN)
- University of Leeds (GB)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-07
- Volume
- 15
- Issue
- 7
- Pages
- 3856-3863
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2933532955
- Cited by
- 102
- References
- 37
- Citations by year
- 2026: 13 · 2025: 9 · 2024: 11 · 2023: 25 · 2022: 11 · 2021: 16 · 2020: 15 · 2019: 2
- FWCI
- 6.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61603316, 61733015, 61773323)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- high-speed train bogie, fault diagnosis, convolutional neural networks, fault classification
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