A Meta-Learning Method for Electric Machine Bearing Fault Diagnosis Under Varying Working Conditions With Limited Data
Author:
Affiliation:
1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
2. Department of Energy Technology, Aalborg University, Aalborg, Denmark
Funder
Sichuan Science and Technology Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10064202/09749912.pdf?arnumber=9749912
Reference32 articles.
1. Gaussian Process Kernel Transfer Enabled Method for Electric Machines Intelligent Faults Detection With Limited Samples
2. Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
3. DCNN-Based Multi-Signal Induction Motor Fault Diagnosis
4. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis
5. On Powers of Gaussian White Noise
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