A hierarchical method based on improved deep forest and case-based reasoning for railway turnout fault diagnosis

Author:

Zhang Yao,Xu Tianhua,Chen Cong,Wang Guang,Zhang Zhizhe,Xiao Tian

Funder

China Railway

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Engineering,General Materials Science

Reference34 articles.

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2. Power-quality impact assessment for high-speed railway associated with high-speed trains using train timetable–part i: Methodology and modeling;Hu;IEEE Trans. Power Delivery,2016

3. Lin Chuang, Lu Jiahua, Lei Jiang, Yuming Shen, Xuemin (Sherman), Stochastic delay analysis for train control services in next-generation high-speed railway communications system, IEEE Trans. Intell. Transport. Syst. (2016).

4. C. Letot, M. Pugnaloni, P. Dehombreux, F. Guillaume, D. Cyril, L. Piero, A data driven degradation-based model for the maintenance of turnouts: a case study, IFAC-PapersOnLine 48 (09 2015).

5. Z. Kai, The railway turnout fault diagnosis algorithm based on bp neural network, in: IEEE International Conference on Control Science & Systems Engineering, 2015.

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