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Received: 2004-07-26

Revision Accepted: 2005-03-15

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.10 P.1030~1039


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

Author(s):  HU Zhong-hui, CAI Yun-ze, LI Yuan-gui, XU Xiao-ming

Affiliation(s):  Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   huhzh@sjtu.edu.cn

Key Words:  Data fusion, Fault diagnosis, Multi-class classification, Multi-class Support Vector Machines, Diesel engine

HU Zhong-hui, CAI Yun-ze, LI Yuan-gui, XU Xiao-ming. Data fusion for fault diagnosis using multi-class Support Vector Machines[J]. Journal of Zhejiang University Science A, 2005, 6(10): 1030~1039.

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journal="Journal of Zhejiang University Science A",
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%T Data fusion for fault diagnosis using multi-class Support Vector Machines
%A HU Zhong-hui
%A CAI Yun-ze
%A LI Yuan-gui
%A XU Xiao-ming
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A1030

T1 - Data fusion for fault diagnosis using multi-class Support Vector Machines
A1 - HU Zhong-hui
A1 - CAI Yun-ze
A1 - LI Yuan-gui
A1 - XU Xiao-ming
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 10
SP - 1030
EP - 1039
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A1030

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 processed 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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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