Fault Detection and Diagnosis of Nonlinear Processes Based on Kernel ICA-KCCA

摘要：

Fault detection and diagnosis based on multivariate statistical way is a hotspot in recent years. According to the nonlinear property of Continuous Annealing Line, this article developes a nonlinear ICA, which combined the predominance of ICA and reproducing kernel Hilbert space, to monitor process. This method has better statistical attribute than traditional ICA algorithm based on maximum negentropy, and it performs more robust and flexible to the variety of signal source. At last, the simulation results of practical production reveal that the kernel ICA-KCCA algorithm is more effective than traditional ICA method.

关键词： Independent Component Analysis Canonical Correlation Analysis Kernel Space Nonlinear Processes Fault Detection and Diagnosis

作者: Shuai Tan Fuli Wang Yuqing Chang Weidong Chen Jiazhuo Xu

作者单位: School of Information Science and Engineering, Northeastern University, Shenyang, 110004 School of Information Science and Engineering, Northeastern University, Shenyang, 110004 Key Laborat Baosteel Industry Inspection Corp.,Shanghai 201900

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3869-3874

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）