Classification of Bearing Faults in Induction Motors with the Hilbert-Huang Transform and Feature Selection-Reference-Cited by-同舟云学术

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Classification of Bearing Faults in Induction Motors with the Hilbert-Huang Transform and Feature Selection

Published:2023-08-28 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)
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Author:

Bórnea Yuri P.¹,Vitor Avyner L. O.¹,Castoldi Marcelo F.¹,Goedtel Alessandro¹,Souza Wesley A.¹

Affiliation:

1. Federal University of Technology-Parana,Department of Electrical Engineering,Cornelio Procopio,PR,Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10271381/10271407/10271440.pdf?arnumber=10271440

Reference28 articles.

1. An FPGA-Based Embedded Robust Speech Recognition System Designed by Combining Empirical Mode Decomposition and a Genetic Algorithm

2. Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework

3. Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

4. Bearing data center,0

5. Machine Learning-Based Fault Diagnosis for Single- and Multi-Faults in Induction Motors Using Measured Stator Currents and Vibration Signals

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