A Novel Data Generation and Quantitative Characterization Method of Motor Static Eccentricity With Adversarial Network
Author:
Affiliation:
1. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China
Funder
Delta Power Electronics Science and Education Development Program of Delta Group
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/63/10130046/10103658.pdf?arnumber=10103658
Reference14 articles.
1. Fault Feature Recovery With Wasserstein Generative Adversarial Imputation Network With Gradient Penalty for Rotating Machine Health Monitoring Under Signal Loss Condition
2. Health Indicator Construction Method of Bearings Based on Wasserstein Dual-Domain Adversarial Networks Under Normal Data Only
3. Towards Advanced Diagnosis Recognition for Eccentricities Faults: Application on Induction Motor
4. Accurate Prediction of Magnetic Field and Magnetic Forces in Permanent Magnet Motors Using an Analytical Solution
5. Investigation of Effects of Asymmetries on the Performance of Permanent Magnet Synchronous Machines
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