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