Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network-Reference-Cited by-同舟云学术

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Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network

Published:2022-11-29 Issue: Volume: Page:
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Container-title:2022 25th International Conference on Electrical Machines and Systems (ICEMS)
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Author:

Kumar Rahul R¹,Randazzo Vincenzo²,Cirrincione Giansalvo³,Cirrinicone Maurizio¹

Affiliation:

1. The University of the South Pacific,School of Information Technology, Engineering, Mathematics and Physics,Suva,Fiji

2. Polytechnic University of Turin,Department of Electronics and Telecommunications,Turin,Italy

3. University of Picardie Jules Verne,Laboratory of Novel Technologies,Amiens,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9982721/9982797/09983026.pdf?arnumber=9983026

Reference13 articles.

1. The on-line curvilinear component analysis (onCCA) for real-time data reduction

2. Growing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary Data;cirrincione;WIRN 2016 26th Italian Workshop on Neural Networks Vietri sul Mare Salerno (Italy),2015

3. Competitive hebbian learning: Algorithm and demonstrations

4. Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park's vector approach

5. The Growing Curvilinear Component Analysis (GCCA) neural network

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. Phase Selection and Location Method of Generator Stator Winding Ground Fault Based on BP Neural Network;Energies;2023-02-03

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