Semisupervised Kernel Learning for FDA Model and its Application for Fault Classification in Industrial Processes

Abstract

For fault classification in industrial processes, the performance of the classification model highly depends on the size of labeled dataset. Unfortunately, labeling the fault types of data samples need expert experiences and prior knowledge of the process, which is costly and time consuming. As a result, semisupervised modeling with both labeled and unlabeled data have recently become an interest in industrial processes. In this paper, a kernel-driven semisupervised fisher discriminant analysis (FDA) model is proposed for nonlinear fault classification. Two discriminant analytical strategies are introduced for online fault assignment, namely k-nearest neighborhood and Bayesian inference. Detailed comparative studies are carried out through two industrial benchmark processes between the linear and kernel-driven semisupervised FDA models, in which the best fault classification performance is obtained by the kernel semisupervised model with Bayesian inference as its discriminant strategy.