| Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis |
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| 作者姓名： | 蔡云泽 胡中辉 尹汝泼 李烨 许晓鸣 |
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| 作者单位： | Department of Automation, Shanghai Jiaotong Universtiy , Shanghai 200030 |
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| 基金项目： | 国家重点基础研究发展计划(973计划);国家高技术研究发展计划(863计划) |
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| 摘 要： | To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines （MPSVMs） using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.
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| 关 键 词： | 数据融合 支持向量机 贝叶斯理论 故障诊断 MPSVMs |
| 收稿时间： | 2004-12-14 |
Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis |
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| CAI Yun-ze,HU Zhong-hui,YIN Ru-po,LI Ye,XU Xiao-ming.Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis[J].Journal of Donghua University,2006,23(1):12-17. |
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| Authors: | CAI Yun-ze HU Zhong-hui YIN Ru-po LI Ye XU Xiao-ming |
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| Abstract: | To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis. |
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| Keywords: | support vector machines data fusion Bayesian theory fault diagnosis |
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