Statistical Neural Networks for Induction Machine Fault Diagnosis and Features Processing Based on Principal Component Analysis-Reference-Cited by-同舟云学术

Statistical Neural Networks for Induction Machine Fault Diagnosis and Features Processing Based on Principal Component Analysis

Published:2021-01-24 Issue:2 Volume:16 Page:307-314
ISSN:1931-4973
Container-title:IEEJ Transactions on Electrical and Electronic Engineering
language:en
Short-container-title:IEEJ Trans Elec Electron Eng

Author:

Marmouch Sameh¹²,Aroui Tarek¹²,Koubaa Yassine²

Affiliation:

1. Université de Sousse Ecole Nationale d'Ingénieurs de Sousse 4054 Sousse Tunisia

2. Université de Sfax Ecole Nationale d'Ingénieurs de Sfax, Laboratoire des Sciences et Techniques de l'Automatiques et de l'informatique industrielle 3038 Sfax Tunisia

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.23298

Reference27 articles.

1. A review and comparison of fault detection and diagnosis methods for squirrel‐cage induction motors: state of the art;Liu Y;ISA Transactions,2017

2. A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor;Shi P;Mechanical Systems and Signal Processing,2014

3. Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM‐CART model;Seera M;IEEE Transactions on Neural Networks and Learning Systems,2012

4. Bearing fault detection of induction motor using wavelet and support vector machines (SVMs);Konar P;Applied Soft Computing Journal,2011

5. Methodology for fault detection in induction motors via sound and vibration signals;Delgado‐Arredondo PA;Mechanical Systems and Signal Processing,2016

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