DocumentCode :
3274581
Title :
Textile spinning-frame roller fault diagnosis based on improved wavelet analysis and support vector machine
Author :
Zhu, Mingling ; Wang, Zhijie
Author_Institution :
Inf. Sci. & Technol., Donghua Univ., Shanghai, China
fYear :
2011
fDate :
15-17 April 2011
Firstpage :
786
Lastpage :
790
Abstract :
For roller fault diagnosis problems in textile spinning-frame, a method which combines an improved wavelet algorithm and support vector machine is presented. This method extracts feature information of the failure and classifies the failure. Based on the analysis of the frequency overlapping phenomenon encountered in traditional wavelet algorithm, the improved wavelet algorithm combines wavelet transform and FFT analysis to reduce the frequency overlapping phenomenon. The SVM classifiers are adopted because the sample available in the problem of roller fault diagnosis is small. Simulation results demonstrate the improved wavelet algorithm is superior to the common wavelet algorithm for the problem of the spinning-frame roller fault diagnosis, and SVM is very effective for fault identification.
Keywords :
condition monitoring; fast Fourier transforms; fault diagnosis; feature extraction; pattern classification; production engineering computing; rollers (machinery); spinning (textiles); support vector machines; textile industry; wavelet transforms; FFT analysis; SVM classifiers; fast Fourier transforms; feature extraction; support vector machine; textile spinning frame roller fault diagnosis; wavelet transform; Algorithm design and analysis; Classification algorithms; Fault diagnosis; Feature extraction; Support vector machines; Wavelet analysis; Wavelet transforms; fault diagnosis; mallat algorithm; support vector machine; vibration signal; wavelet analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
Type :
conf
DOI :
10.1109/ICEICE.2011.5777323
Filename :
5777323
Link To Document :
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