Online Stator Interturn Fault Detection Using Hilbert Transform and Neural Network for Traction Motors of Urban Rail Vehicles-Reference-Cited by-同舟云学术

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Online Stator Interturn Fault Detection Using Hilbert Transform and Neural Network for Traction Motors of Urban Rail Vehicles

Published:2024-08 Issue:4 Volume:12 Page:4236-4248
ISSN:2168-6777
Container-title:IEEE Journal of Emerging and Selected Topics in Power Electronics
language:
Short-container-title:IEEE J. Emerg. Sel. Topics Power Electron.

Author:

Cao Hu¹^ORCID,Zhang Xuhao²^ORCID,Wang Mengqian²,Huo Ke²^ORCID,Wang Huai³^ORCID,Feng Xiaoyun¹

Affiliation:

1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China

2. Technology Centre, CRRC Qingdao Sifang Rolling Stock Research Institute Company Ltd., Qingdao, China

3. Department of Energy Technology, Aalborg University, Aalborg, Denmark

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6245517/10621840/10538263.pdf?arnumber=10538263

Reference35 articles.

1. Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

2. A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems

3. Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors

4. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review

5. Transient model for induction machines with stator winding turn faults

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