Fault Diagnosis of Industrial Process Based On KICA and LSSVM

摘要：

　　The paper combines kernel independent component analysis for establishing the fault detection model and the least squares support vector machine for establishing the fault diagnosis model to set up the industrial process monitor model as the growing difficult for the complex industrial process monitoring.It uses the data collected by the industrial process to extract the nonlinear independent component for establishing the detection model,and put the data into the model of LSSVM to identify the fault only when the fault occurs.Finally,using the data of TE process verifies the validity and practical of the method.

关键词： fault detection fault classification KICA LSSVM

作者: Xiaoya Zhang Xiaodong Wang Yugang Fan Jiande Wu

作者单位: Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunmi Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunmi

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 3802-3807

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）