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)

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
İsim Orijinal	Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition
Yazar	mohammad heidari
Basım Yeri	- Semnan University
Tür	Süreli Yayın
Dil	Arapça
Dijital	Evet
Yazma	Hayır
Kütüphane:	UMA Kütüphaneleri - Malaga Üniversitesi
Demirbaş Numarası	ISSN: 2008-4854, EISSN: 2783-2538, DOI: 10.22075/jme.2022.26634.2244
Kayıt Numarası	cdi_doaj_primary_oai_doaj_org_article_c4b810cae884492d8b0567bb927c2f7e
Lokasyon	Available Online
Notlar	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.
Örnek Metin	مجله مدل سازی در مهندسی, 1402-01, Vol.21 (72), p.147-158

Kaynağa git UMA Kütüphaneleri - Malaga Üniversitesi Bibliotecas UMA - Universidad de Málaga

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

UMA Kütüphaneleri - Malaga Üniversitesi

Kaynağa git

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)

Yazar mohammad heidari

Basım Yeri - Semnan University

Tür Süreli Yayın

Dil Arapça

Dijital Evet

Yazma Hayır

Kütüphane UMA Kütüphaneleri - Malaga Üniversitesi

Demirbaş Numarası ISSN: 2008-4854, EISSN: 2783-2538, DOI: 10.22075/jme.2022.26634.2244

Kayıt Numarası cdi_doaj_primary_oai_doaj_org_article_c4b810cae884492d8b0567bb927c2f7e

Lokasyon Available Online

Notlar 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.

Örnek Metin مجله مدل سازی در مهندسی, 1402-01, Vol.21 (72), p.147-158

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 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

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