NDLI: Machine learning method with compensation distance technique for gear fault detection

Content Provider	IEEE Xplore Digital Library
Author	Zhixin Yang Jianhua Zhong Wong, S.F.
Copyright Year	2011
Description	Author affiliation: Department of Electromechanical Engineering, University of Macau, Padre Tomás Pereira Taipa, China (Zhixin Yang; Jianhua Zhong; Wong, S.F.)
Abstract	In this paper, a condition monitoring and fault diagnosis method for rotating machineries using machine learning technologies including artificial neural network (ANNs) and support vector machine (SVMs) is described. The vibration signal is acquired from gearbox used in local power generation industry for analysis of potential defects. Wavelet packet transforms (WPT) and time domains statistical are used to extraction features, and the compensation distance evaluation technique (CDET) is applied to select optimal feature via sensitivities ranking. A comparative experiment study of the efficiency of ANN and SVM in predication of failure is carried out. The results reveal that the proposed feature selection and machine learning algorithms could be effectively used automatic diagnosis of gear faults.
Starting Page	632
Ending Page	637
File Size	1812922
Page Count	6
File Format	PDF
ISBN	9781612846989
e-ISBN	9781612847009
DOI	10.1109/WCICA.2011.5970591
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gears Feature extraction Artificial neural networks Accuracy Testing Vibrations Support vector machines Gearbox faults diagnosis Distance evaluation technique Artificial neural network
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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