A nonlinear quality-related fault detection approach based on modified kernel partial least squares.
In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method.
Bohai University, Jinzhou 121013, China. Electronic address: jiaojianfangheu@163.com.
Jianfang
J
Jiao
Harbin Institute of Technology, 150001 Harbin, China. Electronic address: joyning@126.com.
Ning
N
Zhao
Bohai University, Jinzhou 121013, China. Electronic address: guang.wang@hit.edu.cn.
Guang
G
Wang
Harbin Institute of Technology, 150001 Harbin, China. Electronic address: shen.yin2011@googlemail.com.
Shen
S
Yin
Data-driven; Fault detection; Kernel partial least squares; Nonlinear monitoring; Quality-related; Singular Value Decomposition