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Automatic detection and classification of rotor cage faults in squirrel cage induction motor

Neural Computing and Applications · Temmuz 2010

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YÖKSİS Kayıtları
Automatic detection and classification of rotor cage faults in squirrel cage induction motor
Neural Computing and Applications · 2010 SCI-Expanded 6 atıf
Prof. Dr. HAYRİ ARABACI →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Automatic detection and classification of rotor cage faults in squirrel cage induction motor
2010 ISSN: 0941-0643 SCI-Expanded 6 atıf
Prof. Dr. HAYRİ ARABACI →
Short term load forecasting using fuzzy logic and ANFIS
2015 ISSN: 0941-0643 SCI-Expanded 1 atıf
Dr. Öğr. Üyesi HASAN HÜSEYİN ÇEVİK →
Determination of induction motor parameters with differential evolution algorithm
2012 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Short term load forecasting using fuzzy logic and ANFIS
2015 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Fuzzy logic based induction motor protection system
2013 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Determination of induction motor parameters with differential evolution algorithm
2012 ISSN: 0941-0643 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
Cost optimization of mixed feeds with the particle swarm optimization method
2013 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
A combination of Genetic Algorithm Particle Swarm Optimization and Neural Network for palmprint recognition
2013 ISSN: 0941-0643 SCI-Expanded 1 atıf
Prof. Dr. ADEM ALPASLAN ALTUN →
Cost optimization of mixed feeds with the particle swarm optimization method
2013 ISSN: 0941-0643 SCI-Expanded
Doç. Dr. MEHMET AKİF ŞAHMAN →
Fuzzy logic based induction motor protection system
2013 ISSN: 0941-0643 SCI
Dr. Öğr. Üyesi OKAN UYAR →
A new MILP model proposal in feed formulation and using a hybrid linear binary PSO H LBP approach for alternative solutions
2018 ISSN: 0941-0643 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
FPGA based self organizing fuzzy controller for electromagnetic filter
2016 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
A new MILP model proposal in feed formulation and using a hybrid linear binary PSO H LBP approach for alternative solutions
2016 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech
2018 ISSN: 0941-0643 SCI-Expanded Q1
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
A new denoising method for fMRI based on weighted three-dimentional wavelet transform
2018 ISSN: 0941-0643 SCI-Expanded
Dr. Öğr. Üyesi GÜZİN ÖZMEN →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
A new MILP model proposal in feed formulation and using a hybrid-linear binary PSO (H-LBP) approach for alternative solutions
2018 ISSN: 0941-0643 SCI-Expanded
Prof. Dr. ADEM ALPASLAN ALTUN →
Hybrid breast cancer detection tem via neural network and feature ion based on SBS SFS and PCA
2013 ISSN: 0941-0643 SCI-Expanded 5 atıf
Dr. Öğr. Üyesi ONUR İNAN →
FPGA-based self-organizing fuzzy controller for electromagnetic filter
2017 ISSN: 0941-0643 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →

Makale Bilgileri

Dergi Neural Computing and Applications
ISSN09410643
Yayın TarihiTemmuz 2010
Cilt / Sayfa19 · 713-723
DOI10.1007/s00521-009-0330-7
Scopus ID2-s2.0-77953912939
Özet The detection of broken rotor bars and broken end-ring in three-phase squirrel cage induction motors by means of improved decision structure. The structure consists of current signal analysis (CSA), Artificial Neural Network (ANN) and diagnosis algorithm. Effects of broken bars and end-ring on current signal and feature extraction are in the CSA. The rotor cage faults are classified by using ANN. And result matrixes of ANN are considered two different ways for diagnosis. Then the diagnoses are compared with each other. In this study six different rotor faults, which are one, two, three broken bars, bar with high resistance, broken end-ring and healthy rotor, are investigated. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated by analyzing side-bands in current spectrum. To reduce bad effects of changing of distance between the side-band and main component on the detection and classification of the faults, the spectrum is achieved with low definition. Thus, the improved decision structure diagnoses faulted rotors with 100% accuracy and classified rotor faults 98.33% accuracy. © Springer-Verlag London Limited 2009.

Yazarlar (2)

1
Hayri Arabaci
2
Osman Bilgin

Anahtar Kelimeler

Fault diagnosis Fourier analysis Neural network Rotor faults Squirrel cage induction motor

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Neural Computing and Applications
Q1
SJR Skoru1,102
H-Index146
YayıncıSpringer London
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Software (Q1)
Dergi sayfasına git

Metrikler

36
Atıf
2
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

HA
HAYRİ ARABACI
Prof. Dr.

Hızlı Erişim

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