Contrast Feature-Based Approach for Fault Detection in Wound-Rotor Induction Machines-Reference-Cited by-同舟云学术

Contrast Feature-Based Approach for Fault Detection in Wound-Rotor Induction Machines

Published:2022-09-05 Issue: Volume: Page:
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Container-title:2022 International Conference on Electrical Machines (ICEM)
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Author:

Ferrucho-Alvarez Edna R.¹,Taherzadeh Mehdi²,Henao Humberto²,Capolino Gerard-Andre²,Cabal-Yepez Eduardo¹

Affiliation:

1. University of Guanajuato,Engineering Division, Campus IrapuatoSalamanca,Department of Multidisciplinary Studies,Yacatitas, Yuriria Guanajuato,Mexico,38944

2. University of Picardie "Jules Verne",LTI Lab,Department of Electrical Engineering,Amiens,France,80000

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9910604/9910578/09910754.pdf?arnumber=9910754

Reference21 articles.

1. Vibration monitoring for wound rotor induction machine winding fault detection

2. Fault Classification and Fault Signature Production for Rolling Element Bearings in Electric Machines

3. Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

4. Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor

5. Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Cited by 2 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. Fault Diagnosis Using Imbalanced Data of Rolling Bearings Based on a Deep Migration Model;IEEE Access;2024

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