Journal
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
Volume 13, Issue 3, Pages 83-88Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MITS.2019.2907629
Keywords
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Funding
- National Natural Science Foundation of China [61490703, 61573180, 61503181]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Funding of Jiangsu Innovation Program for Graduate Education [KYLX16-0378]
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This paper introduces a new fault detection and diagnosis (FDD) architecture for high-speed trains, based on a modified broad learning system (BLS) which enables fast and accurate FDD through effective feature extraction for online implementation. The proposed architecture is scalable, allowing for the development of multiple FDD methods.
This paper proposes a new fault detection and diagnosis (FDD) architecture for high-speed trains, whose core is a modified broad learning system (BLS). This architecture is a data-driven realization, which enables fast and accurate FDD by effective feature extraction for online implementation. Under the proposed architecture, multiple FDD methods can be developed because of its inherent scalability.
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