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

被引：11

作者：

Hu Z.-H. ^{[1
]}

Cai Y.-Z. ^{[1
]}

Li Y.-G. ^{[1
]}

Xu X.-M. ^{[1
]}

机构：

[1] Department of Automation, Shanghai Jiaotong University

来源：

Journal of Zhejiang University-SCIENCE A | 2005年 / 6卷 / 10期

关键词：

Data fusion; Diesel engine; Fault diagnosis; Multi-class classification; Multi-class support vector machine;

D O I：

10.1631/jzus.2005.A1030

中图分类号：

学科分类号：

摘要：

Multi-source multi-class classification methods based on multi-class support vector machines and data fusion strategies are proposed. 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 a 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.

引用

页码：1030 / 1039

页数：9

共 19 条

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