Three-Phase Asynchronous Motor Fault Diagnosis Using Attention Mechanism and Hybrid CNN-MLP By Multi-Sensor Information
Author:
Affiliation:
1. School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China
Funder
National Natural Science Foundation of Zhejiang Two Integration Joint Fund Key Projects
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10227288.pdf?arnumber=10227288
Reference35 articles.
1. Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions
2. Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN
3. Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor
4. An adaptive deep convolutional neural network for rolling bearing fault diagnosis
5. Fault diagnosis of single-phase induction motor based on acoustic signals
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