Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Name: Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition
Author: mohammad heidari

İsim	Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition
Yazar	mohammad heidari
Basım Tarihi:	1401
Basım Yeri	- Semnan University, 1401.
Konu	wavelet transform, empirical mode decomposition, wavelet support vector machine, genetic algorithm, Engineering design, TA174
Tür	Süreli Yayın
Dil	Farsça
Dijital	Evet
Yazma	Hayır
Fiziksel Boyutlar	electronic resource
Kütüphane:	Bursa Uludağ Üniversitesi Kütüphanesi
Demirbaş Numarası	edsdoj.4b810cae884492d8b0567bb927c2f7e
Kayıt Numarası	mcjvfjmdsn
Lokasyon	LCC:Engineering design
Tarih	1401
Örnek Metin	In this study, a comparison among the empirical mode decomposition, ensemble empirical mode decomposition and Morlet continuous wavelet transform in fault diagnosis of bearings are performed. A Morlet wavelet support vector machine with one against one strategy that was optimized by a genetic algorithm was used for fault classification. A scale selection criterion based on the maximum relative energy to Renyi entropy ratio is proposed to determine the optimal decomposition scale for wavelet analysis. A comparison between the performances of optimized and non-optimized of support vector machines were also carried out. Vibration signals were collected by a test rig for different fault of a bearing such as normal case, bearing with inner and outer race fault, and bearing with ball fault and combine fault. After the processing of vibration signals their frequency components, several statistical features were extracted from each frequency component as input of wavelet support vector machine for the fault classification of ball bearings. For reducing of time and process of fault diagnosis, optimum feature sets of statistical parameters are selected by Utans method. K-fold cross validation method is used for evaluation of classifier. The results show that continuous wavelet transform with Morlet base has higher accuracy with respect to other methods in fault classification of bearings.
DOI	10.22075/jme.2022.26634.2244
ISSN	2008-4854, 2783-2538
İlişki	https://modelling.semnan.ac.ir/article_7307_43c0c7deef0ab46d26611584da99dfbc.pdf; https://doaj.org/toc/2008-4854; https://doaj.org/toc/2783-2538

Kaynağa git Bursa Uludağ Üniversitesi Kütüphanesi

Diğer Nüshalar

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Detayları görüntüle

Aramaya Dön

Bursa Uludağ Üniversitesi Kütüphanesi

Kaynağa git

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Yazar mohammad heidari

Basım Tarihi 1401

Basım Yeri - Semnan University, 1401.

Konu wavelet transform, empirical mode decomposition, wavelet support vector machine, genetic algorithm, Engineering design, TA174

Tür Süreli Yayın

Dil Farsça

Dijital Evet

Yazma Hayır

Fiziksel Boyutlar electronic resource

Kütüphane Bursa Uludağ Üniversitesi Kütüphanesi

Demirbaş Numarası edsdoj.4b810cae884492d8b0567bb927c2f7e

Kayıt Numarası mcjvfjmdsn

Lokasyon LCC:Engineering design

Tarih 1401

Örnek Metin In this study, a comparison among the empirical mode decomposition, ensemble empirical mode decomposition and Morlet continuous wavelet transform in fault diagnosis of bearings are performed. A Morlet wavelet support vector machine with one against one strategy that was optimized by a genetic algorithm was used for fault classification. A scale selection criterion based on the maximum relative energy to Renyi entropy ratio is proposed to determine the optimal decomposition scale for wavelet analysis. A comparison between the performances of optimized and non-optimized of support vector machines were also carried out. Vibration signals were collected by a test rig for different fault of a bearing such as normal case, bearing with inner and outer race fault, and bearing with ball fault and combine fault. After the processing of vibration signals their frequency components, several statistical features were extracted from each frequency component as input of wavelet support vector machine for the fault classification of ball bearings. For reducing of time and process of fault diagnosis, optimum feature sets of statistical parameters are selected by Utans method. K-fold cross validation method is used for evaluation of classifier. The results show that continuous wavelet transform with Morlet base has higher accuracy with respect to other methods in fault classification of bearings.

DOI 10.22075/jme.2022.26634.2244

ISSN 2008-4854, 2783-2538

İlişki https://modelling.semnan.ac.ir/article_7307_43c0c7deef0ab46d26611584da99dfbc.pdf; https://doaj.org/toc/2008-4854; https://doaj.org/toc/2783-2538

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Diğer Nüshalar

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

Diğer Nüshalar

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...

Fault Diagnosis of Ball Bearings by W...