Cited by 173Transportation Systems and SafetyMachine Fault Diagnosis TechniquesFault Detection and Control Systems

Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach

Yin, Jiateng, Zhao, Wentian·2016·Engineering Applications of Artificial Intelligence

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Crossref · 10.1016/j.engappai.2016.10.002

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28 of 28 references verified0 citation errors · 0 not checkable

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Authors
Yin, J., & Zhao, W.
Affiliations
  • University of Wisconsin–Madison (US)
  • Beijing Urban Construction Design & Development Group (China) (CN)
Corresponding author
Yin, Jiateng
Venue
Engineering Applications of Artificial Intelligence
Publisher
Elsevier BV
Published
2016-11
Volume
56
Pages
250-259
ISSN
0952-1976 (ISSN-L)
OpenAlex
W2530806392
Cited by
173
References
28
Citations by year
2026: 6 · 2025: 15 · 2024: 23 · 2023: 15 · 2022: 21 · 2021: 18 · 2020: 20 · 2019: 30 · 2018: 17 · 2017: 8
FWCI
57.01
Type
journal article
Language
en
Open access
closed
Locations
Engineering Applications of Artificial Intelligence (publishedVersion)
Funders
Fundamental Research Funds for the Central Universities (2015YJS026)
Topics
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Transportation Systems and Safety
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Keywords
fault diagnosis, high-speed rail, deep belief network, restricted Boltzmann machine, high-speed trains

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