A Comprehensive Interturn Fault Severity Diagnosis Method for Permanent Magnet Synchronous Motors Based on Transformer Neural Networks-Reference-Cited by-同舟云学术

A Comprehensive Interturn Fault Severity Diagnosis Method for Permanent Magnet Synchronous Motors Based on Transformer Neural Networks

Published:2023-11 Issue:11 Volume:19 Page:10923-10933
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Parvin Farbod¹^ORCID,Faiz Jawad¹^ORCID,Qi Yuan²^ORCID,Kalhor Ahmad³^ORCID,Akin Bilal²^ORCID

Affiliation:

1. Center of Excellence on Applied Electromagnetic Systems, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

2. Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX, USA

3. Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10255543/10038556.pdf?arnumber=10038556

Reference34 articles.

1. A Novel Imbalanced Data Classification Method Based on Weakly Supervised Learning for Fault Diagnosis

2. Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network

3. Interpretable Convolutional Neural Network Through Layer-wise Relevance Propagation for Machine Fault Diagnosis

4. Stack Autoencoder Transfer Learning Algorithm for Bearing Fault Diagnosis Based on Class Separation and Domain Fusion

5. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

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