Cited by 102Machine Fault Diagnosis TechniquesFault Detection and Control SystemsStructural Health Monitoring Techniques

Fault diagnosis of high-speed train bogie by residual-squeeze net

Su, Liyuan, Ma, Lei, Qin, Na, Huang, Deqing, Kemp, Andrew H.·2019·IEEE Transactions on Industrial Informatics

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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Crossref · 10.1109/tii.2019.2907373

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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
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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