Advanced feature selection for broken rotor bar faults in induction motors
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/8362637/8369960/08369981.pdf?arnumber=8369981
Cited by 12 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. Optimization of Practicality for Modeling- and Machine Learning-Based Framework for Early Fault Detection of Induction Motors;Energies;2024-07-28
2. Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM;Electronics;2024-03-30
3. Development a Sensor System for Sensing Rotating Machines Fault of Heavy Industries;2023 6th International Conference on Electrical Information and Communication Technology (EICT);2023-12-07
4. MINDTwin AI: Multiphysics Informed Digital-Twin for Fault Localization in Induction Motor Using AI;2023 International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE);2023-11-02
5. Vibration Magnitude Analysis on Induction Motors of Different Efficiency Classes Due to Voltage Unbalance;IEEE Transactions on Industry Applications;2023-05
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