Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors-Reference-Cited by-同舟云学术

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Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors

Published:2006-06 Issue:4 Volume:53 Page:1298-1308
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Ayhan B.,Chow M.-Y.,Song M.-H.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx5/41/34916/01667927.pdf?arnumber=1667927

Cited by 106 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. Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection;Sensors;2022-12-05

2. Framework for Reliable Fault Detection with Sensor Data;Studies in Computational Intelligence;2022-09-30

3. Broken Rotor Bars Fault Detection in Induction Machine Using Machine Learning Algorithms;2022 19th International Multi-Conference on Systems, Signals & Devices (SSD);2022-05-06

4. Weighted quantile discrepancy-based deep domain adaptation network for intelligent fault diagnosis;Knowledge-Based Systems;2022-03

5. Finite Element Analysis for Fault Diagnosis in Broken Rotor Bar of a Polyphase Induction Motor;2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T);2022-03-01

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