Distributed Bearing Fault Classification of Induction Motors Using 2-D Deep Learning Model
Author:
Affiliation:
1. Yuzuncu Yil University, Van, Turkey
2. University of Texas at Dallas, Richardson, TX, USA
3. Texas A&M University, College Station, TX, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Medicine
Link
http://xplorestaging.ieee.org/ielx7/8847244/10384486/10286268.pdf?arnumber=10286268
Reference33 articles.
1. Advances in Diagnostic Techniques for Induction Machines
2. Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016
3. Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network
4. Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
5. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
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1. Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current;Machines;2024-07-27
2. A Multi-Input Convolutional Neural Network Model for Electric Motor Mechanical Fault Classification Using Multiple Image Transformation and Merging Methods;Machines;2024-02-02
3. Real-Time Current-Based Distributed Bearing Faults Detection in Small Cooling Fan Motors;IEEE Transactions on Industry Applications;2023
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