A fault diagnosis method based on improved kernel Fisher

A fault diagnosis method of kernel Fisher projection was proposed to solve issues of nonlinear data, complex classes, and difficult fault-diagnosing in chemical processes. The proposed method provided a uniform solution for partial sample mix-up induced by a large difference in category distances and nebulous classification of different category's boundary data after projection of the original data sample. First, an improved category distance was used to change sample distribution in the projection space so that the sample had a good projection. Then, boundary data was screened out by a defined threshold parameter and classified by improved K-Nearest Neighbor (K-NN) algorithm, which none-boundary data was classified by Mahalanobis distance. Simulation in a TE process showed that training accuracy was increased by 2.25% and testing accuracy was increased by 2.41% in the first experiment, whereas training accuracy was increased by 4.75% and testing accuracy was increased by 6.75% in the second experiment. Therefore, the method improved both fault diagnosis time and accuracy.