Active Learning of Support Vector Machine for Fault Diagnosis of Bearings

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.

Authors and Affiliations

1. 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

Editors and Affiliations

1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

   Zhang Yi

3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

   Jacek M. Zurada

4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

   Bao-Liang Lu

5. School of Electrical and Electronic Engineering, University of Manchester, UK

   Hujun Yin

Reprints and permissions

© 2006 Springer-Verlag Berlin Heidelberg

Policies and ethics