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 Yeri	- Semnan University
Konu	مجله مدل سازی در مهندسی, 1402-01, Vol.21 (72), p.147-158
Tür	Süreli Yayın
Dil	Farsça
Dijital	Evet
Yazma	Hayır
Kütüphane:	Leicester Üniversitesi Kütüphanesi
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	DOAJ Directory of Open Access Journals
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.

Kaynağa git Leicester Üniversitesi Kütüphanesi The University of Leicester Library

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

Leicester 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 Yeri - Semnan University

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

Tür Süreli Yayın

Dil Farsça

Dijital Evet

Yazma Hayır

Kütüphane Leicester Üniversitesi Kütüphanesi

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 DOAJ Directory of Open Access Journals

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.

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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