Fault Diagnosis Based On Signed Directed Graph and Support Vector Machine

摘要：

Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG;the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

关键词： Support Vector Machine Signed Directed Graph Fault Diagnosis Tennessee-Eastman Process

作者: Xiaoming Han Qing Lv Kerning Xie

作者单位: College of Information Engineering Taiyuan University of Technology, 030024 Taiyuan, Shanxi, China

会议类型: 国际会议

会议名称: 2010 International Conference on Software and Computing Technology(2010年软件与计算机技术国际会议 ICSCT 2010)

会议地点: 昆明

会议语种:英文

页码: 808-811

在线出版日期: 2010-10-17（万方平台首次上网日期，不代表论文的发表时间）