A machine learning strategy for detection of stator winding short-circuit faults in induction motors-Reference-Cited by-同舟云学术

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A machine learning strategy for detection of stator winding short-circuit faults in induction motors

Published:2023-10-18 Issue: Volume:2 Page:1-7
ISSN:
Container-title:2023 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)
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Author:

Leon-Olvera Angel N.¹,Rangel-Rodriguez Angel H.¹,Granados-Lieberman David²,Amezquita-Sanchez Juan P.¹,Valtierra-Rodriguez Martin¹

Affiliation:

1. Universidad Autonoma de Queretaro (UAQ),ENAP-Research Group, CA-Sistemas Dinamicos y Control,San Juan del Rio,Mexico,76807

2. Tecnológico Nacional de México / ITS de Irapuato,ENAP-Research Group, CA-Fuentes Alternas y Calidad de la Energía Eléctrica,Departamento de Ingeniería Electromecánica,Irapuato,Guanajuato,México,36821

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10409267/10409296/10409401.pdf?arnumber=10409401

Reference22 articles.

1. Libro practico de los generadores, transformadores y motores eléctricos;Harper;Editorial Limusa,2004

2. Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

3. Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors

4. A Novel Approach for Broken Bar Fault Diagnosis in Induction Motors Through Torque Monitoring

5. A review of induction motor fault modeling

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