Process Fault Diagnosis Based on Kernel Regularized Fisher Discriminant

摘要：

Fisher discriminant analysis (FDA) is a widely used dimensionality reduction technique for fault diagnosis in industry process, whereas it is difficult to capture nonlinear relationship. Kernel FDA (KFDA) is nonlinear extension of FDA developed in the last ten years. Unfortunately, small sample size (3S) problem will be arisen in both FDA and KFDA. Regularized FDA (RFDA) is an effective solution for this problem. To obtain kernel form of RFDA is basis for solving both nonlinear and 3S problem. In this paper a novel kernel form of RFDA, which is transformed to equation solving problem and expressed in the dual form, is deduced and implement procedure for fault diagnosis is given. Experimental results on the Tennessee Eastman TE process show validity and effectivity of the proposed kernel algorithm for 3s problem. Several relationships between regularization parameter and diagnosis effect are derived at last.

关键词： fault diagnosis regularization Fisher discriminant analysis kernel method

作者: YU Chunmei

作者单位: School of information engineering, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 1941-1945

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）