Nonlinear feature extraction and classification of multivariate data in kernel feature space

Batch processes have played an essential role in the production of high value-added product of chemical, pharmaceutical, food, bio-chemical, and semi-conductor industries. For productivity and quality improvement, several multivariate statistical techniques such as principal component analysis (PCA) and Fisher discriminant analysis (FDA) have been developed to solve a fault diagnosis problem of batch processes. Fisher discriminant analysis, as a traditional statistical technique for feature extraction and classification, has been shown to be a good linear technique for fault diagnosis and outperform PCA based diagnosis methods. This paper proposes a more efficient nonlinear diagnosis method for batch processes using a kernel version of Fisher discriminant analysis (KFDA). A case study on two batch processes has been conducted. In addition, the diagnosis performance of the proposed method was compared with that of an existing diagnosis method based on linear FDA. The diagnosis results showed that the proposed KFDA based diagnosis method outperforms the linear FDA based method.