Discriminant Analysis-Guided Alignment Network for Multimachine Fault Collaborative Learning and Diagnosis-Reference-Cited by-同舟云学术

Discriminant Analysis-Guided Alignment Network for Multimachine Fault Collaborative Learning and Diagnosis

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhong Kai¹^ORCID,Zhang Jiaming¹^ORCID,Xu Yingcheng¹^ORCID,Zhang Haifeng²^ORCID,Huang Darong³^ORCID,Xu Shuiqing⁴^ORCID

Affiliation:

1. Institutes of Physical Science and Information Technology, Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, China

2. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, School of Mathematical Science, Anhui University, Hefei, China

3. Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China

4. College of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China

Funder

National Natural Science Foundation of China

Anhui Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10480303.pdf?arnumber=10480303

Reference32 articles.

1. A Flexible Monitoring Framework via Dynamic-Multilayer Graph Convolution Network

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

3. Novel Convolutional Neural Network (NCNN) for the Diagnosis of Bearing Defects in Rotary Machinery

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

5. Overview of fault prognosis for traction systems in high-speed trains: A deep learning perspective