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Izvorni znanstveni članak
https://doi.org/10.17559/TV-20190516040455

Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm

Junwu Li ; School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Guoning Li ; School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Puni tekst: engleski, pdf (1 MB) str. 1091-1097 preuzimanja: 341* citiraj
APA 6th Edition
Li, J. i Li, G. (2019). Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm. Tehnički vjesnik, 26 (4), 1091-1097. https://doi.org/10.17559/TV-20190516040455
MLA 8th Edition
Li, Junwu i Guoning Li. "Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm." Tehnički vjesnik, vol. 26, br. 4, 2019, str. 1091-1097. https://doi.org/10.17559/TV-20190516040455. Citirano 08.03.2021.
Chicago 17th Edition
Li, Junwu i Guoning Li. "Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm." Tehnički vjesnik 26, br. 4 (2019): 1091-1097. https://doi.org/10.17559/TV-20190516040455
Harvard
Li, J., i Li, G. (2019). 'Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm', Tehnički vjesnik, 26(4), str. 1091-1097. https://doi.org/10.17559/TV-20190516040455
Vancouver
Li J, Li G. Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm. Tehnički vjesnik [Internet]. 2019 [pristupljeno 08.03.2021.];26(4):1091-1097. https://doi.org/10.17559/TV-20190516040455
IEEE
J. Li i G. Li, "Research on Fault Diagnosis of ZPW-2000K Track Circuit Based on RS-BN Algorithm", Tehnički vjesnik, vol.26, br. 4, str. 1091-1097, 2019. [Online]. https://doi.org/10.17559/TV-20190516040455

Sažetak
For problem of complex fault types and uncertain diagnostic features of the ZPW-2000K track circuit, traditional fault diagnosis mainly adopts manual diagnosis methods, which leads to low automatic diagnosis. This paper proposes a fault diagnosis method based on Rough Sets (RS) reduction model and Bayesian Network (BN) structure learning fusion. Firstly, data mining and feature extraction are performed on the fault data table, and expert knowledge is built into the prior knowledge base. Secondly, the K2 algorithm is used to train the fault feature quantity, and the BN model is built by combining the prior knowledge base. Then, a diagnostic decision table is established through the fault instance, and RS is used for attribute reduction, dimensionality reduction, and simplified model. The MLE algorithm is used again to learn the parameters to obtain the conditional probability table of the model, and the complete BN structure is established based on the RS-BN algorithm. Finally, the comparative analysis of the simplified model and the non-simplified model is carried out. Through the experimental simulation of the ZPW-2000K track circuit fault of a high-speed railway station, the accuracy and effectiveness of the diagnostic method are verified.

Ključne riječi
attribute reduction; Bayesian network; fault diagnosis; rough set; ZPW-2000K track circuit

Hrčak ID: 223307

URI
https://hrcak.srce.hr/223307

Posjeta: 573 *