Induction Machine Stator Fault Tracking Using the Growing Curvilinear Component Analysis-Reference-Cited by-同舟云学术

Induction Machine Stator Fault Tracking Using the Growing Curvilinear Component Analysis

Published:2021 Issue: Volume:9 Page:2201-2212
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kumar Rahul R^ORCID,Randazzo Vincenzo^ORCID,Cirrincione Giansalvo,Cirrincione Maurizio^ORCID,Pasero Eros^ORCID,Tortella Andrea^ORCID,Andriollo Mauro^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09306809.pdf?arnumber=9306809

Cited by 11 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. Machine Learning-Based Classification of Stator Inter-Turn Faults Severities Using Time Domain Features and Principal Component Analysis;2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG);2023-12-03

2. Open Rotor Phase Fault Detection in Wound-Rotor Induction Machines Using Signal Texture and Shallow Neural Networks;2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET);2023-04-29

3. Stator Inter-Turn Fault Severity Classification Using Neural Based Approaches;2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET);2023-04-29

4. A Comparative Study for the Detection of Stator Inter-Turn Faults in Induction Motors Using Shallow Neural Networks and Non-Neural Based Techniques;2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET);2023-04-29

5. Abnormal Health Monitoring and Assessment of a Three-Phase Induction Motor Using a Supervised CNN-RNN-Based Machine Learning Algorithm;Mathematical Problems in Engineering;2023-01-30