Self-Supervised Metalearning Generative Adversarial Network for Few-Shot Fault Diagnosis of Hoisting System With Limited Data
Author:
Affiliation:
1. School of Mechanical Engineering, Southeast University, Nanjing, China
2. School of Industrial Engineering, University of Toronto, Toronto, ON, Canada
Funder
National Natural Science Foundation of China
Postgraduate Research and Practice Innovation Program of Jiangsu Province
Fundamental Research Funds for the Central Universities
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/ielx7/9424/10064202/09783112.pdf?arnumber=9783112
Reference38 articles.
1. Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis
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3. Few-shot transfer learning for intelligent fault diagnosis of machine
4. Multiple Kernel Based Transfer Learning for the Few-Shot Recognition Task in Smart Home Scene
5. Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling
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