Author
mohammad heidari
Subject
empirical mode decomposition, genetic algorithm, wavelet support vector machine, wavelet transform
Type
Document
Language
Undetermined
Digital
Yes
Manuscript
No
Library
The Queen's University of Belfast Libraries
Library Asset ID
ISSN: 2008-4854, EISSN: 2783-2538, DOI: 10.22075/jme.2022.26634.2244
Record ID
cdi_doaj_primary_oai_doaj_org_article_c4b810cae884492d8b0567bb927c2f7e
Açıklama
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.
Şuna bir parçasıdır
مجله مدل سازی در مهندسی, 1402-01, Vol.21 (72), p.147-158
Kaynak
DOAJ Directory of Open Access Journals
