NDLI: Fault diagnosis of gear cases based on entropy of singular values and support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Zhang Chao Yang Li-dong Chen Jian-jun
Copyright Year	2011
Description	Author affiliation: School of Electronic-Mechanical Engineering, Xidian University, Xi'an, China (Chen Jian-jun) \|\| School of Information Engineering, University of Science and Technology of Inner Mongol, Bao Tou, China (Zhang Chao; Yang Li-dong)
Abstract	In view of the non-stationary features of vibration signals of gear and the difficulty to obtain a large number of fault samples in practice, a fault diagnosis scheme based on empirical mode decomposition (EMD) entropy of singular values and support vector machine is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs); the initial feature vector matrixes are formed by the intrinsic mode functions. Secondly, using the singular value decomposition technique to the vector matrixes, the singular values are obtained. Finally, the singular values serve as the fault characteristic vectors to be inputted to the support vector machine classifier and the work conditions and fault patterns are identified by the output of the classifier. Practical examples show that the diagnosis approach put forward in this paper can identify gear fault patterns effectively.
Starting Page	4207
Ending Page	4211
File Size	316826
Page Count	5
File Format	PDF
ISBN	9781424480364
e-ISBN	9781424480395
DOI	10.1109/ICEICE.2011.5777219
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Vibrations Intrinsic mode founction Entropy of dingular values Empirical mode decomposition Gears Support vector machine classification Entropy Support vector machine Acceleration
Content Type	Text
Resource Type	Article

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