Condition Monitoring and Fault Diagnosis of Induction Motor Faults using Deep Learning : A Review-Reference-Cited by-同舟云学术

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Condition Monitoring and Fault Diagnosis of Induction Motor Faults using Deep Learning : A Review

Published:2023-09-20 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE AFRICON
language:
Short-container-title:

Author:

Sikinyi Lydiah Aywa¹,Maina Christopher Muriithi²,Ngoo Livingstone³

Affiliation:

1. Masinde Muliro University of Science and Technology,Department of Electrical and Communications Engineering,Kakamega,Kenya

2. Murang'a University of Technology,Department of Electrical and Electronics Engineering,Murang'a,Kenya

3. Multimedia University of Kenya,Department of Electrical and Electronics Engineering,Nairobi,Kenya

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10293181/10293212/10293578.pdf?arnumber=10293578

Reference26 articles.

1. Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

2. A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors

3. Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

4. Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesian Modeling

5. An Automatic Feature Learning and Fault Diagnosis Method Based on Stacked Sparse Autoencoder

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