Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches
Author:
Affiliation:
1. Mitsubishi Electric Research Laboratories,Cambridge,MA,USA,02139
2. Mitsubishi Electric Corporation,Advanced Technology R&D Center,Amagasaki,Japan,661-8661
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10271381/10271407/10271414.pdf?arnumber=10271414
Reference26 articles.
1. Static Eccentricity Fault Detection for PSH-type Induction Motors Considering High-order Air Gap Permeance Harmonics
2. Comprehensive Eccentricity Fault Diagnosis in Induction Motors Using Finite Element Method
3. Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning
4. Semi-Supervised Bearing Fault Diagnosis and Classification Using Variational Autoencoder-Based Deep Generative Models
5. A method for dynamic simulation of air-gap eccentricity in induction machines
Cited by 1 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. Induction Motor Eccentricity Fault Detection and Quantification Using Topological Data Analysis;IEEE Access;2024
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