Active Learning of Support Vector Machine for Fault Diagnosis of Bearings

Zhang, Zhousuo; Lv, Wenzhi; Shen, Minghui

doi:10.1007/11760191_58

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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International Symposium on Neural Networks

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Abstract

Based on traditional Active Support Vector Machine (ASVM), the learning method of Probabilistic Active SVM (ProASVM) is introduced to detect fault of bearings. Compared with the general SVM, the active learning methods can effectively reduce the number of samples on the condition of keeping the classification accuracy. ASVM actively selects data points closest to the current separation hyperplane, while ProASVM selects the points according to the probability of the sample point as a support vector. The two methods are applied to classify the practical vibration signal of bearings and the results show that ProASVM is a better algorithm of classification than ASVM.

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Authors and Affiliations

School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, China
Zhousuo Zhang, Wenzhi Lv & Minghui Shen

Authors

Zhousuo Zhang
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Wenzhi Lv
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Minghui Shen
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Editors and Affiliations

Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Jun Wang
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China
Zhang Yi
Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A
Jacek M. Zurada
Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China
Bao-Liang Lu
School of Electrical and Electronic Engineering, University of Manchester, UK
Hujun Yin

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Zhang, Z., Lv, W., Shen, M. (2006). Active Learning of Support Vector Machine for Fault Diagnosis of Bearings. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_58

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DOI: https://doi.org/10.1007/11760191_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
eBook Packages: Computer ScienceComputer Science (R0)

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