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Multi-Source Classification Method

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Journal of Zhejiang University SCIENCE ISSN 1009-3095 http://www.zju.edu.cn/jzus
E-mail: jzus@zju.edu.cn

          Data fusion for fault diagnosis using multi-class
                      Support Vector Machines*

    HU Zhong-hui (胡中辉)†, CAI Yun-ze (蔡云泽), LI Yuan-gui (李远贵), XU Xiao-ming (许晓鸣)

              (Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, China)
                              †E-mail: huhzh@sjtu.edu.cn
                      Received July 26, 2004; revision accepted Mar. 15, 2005

Abstract: Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc- essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

Key words:   Data fusion, Fault diagnosis, Multi-class classification, Multi-class Support Vector Machines, Diesel engine
doi:10.1631/jzus.2005.A1030 Document code:   A CLC number:...

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