A Reversible Residual Network-Aided Canonical Correlation Analysis to Fault Detection and Diagnosis in Electrical Drive Systems-Reference-Cited by-同舟云学术

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A Reversible Residual Network-Aided Canonical Correlation Analysis to Fault Detection and Diagnosis in Electrical Drive Systems

Published:2024 Issue: Volume:73 Page:1-10
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Wang Shenquan¹^ORCID,Ju Yunfei²^ORCID,Fu Caixin²^ORCID,Xie Pu³^ORCID,Cheng Chao⁴^ORCID

Affiliation:

1. School of Electrical and Electronical Engineering, Changchun University of Technology, Changchun, China

2. School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun, China

3. Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA

4. School of Computer Science and Engineering, Changchun University of Technology, Changchun, China

Funder

National Natural Science Foundation of China

Department of Science and Technology of Jilin Province

Changchun Science and Technology Bureau

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10379534.pdf?arnumber=10379534

Reference35 articles.

1. Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

2. Reliable State Feedback Control of T–S Fuzzy Systems With Sensor Faults

3. Condition Monitoring for Device Reliability in Power Electronic Converters: A Review

4. Adaptive Compensation of Traction System Actuator Failures for High-Speed Trains

5. Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gap-MK-DCCA-Based Intelligent Fault Diagnosis for Nonlinear Dynamic Systems;Processes;2024-02-15

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