Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems

Currently, industry needs more robust fault diagnosis systems. One way to achieve this is to complement these systems with preprocessing modules. This makes possible to reduce the dimension of the work-space by removing irrelevant information that hides faults in development or overloads the system's management. In this paper, a comparison between five performance measures in the adjustment of a Gaussian kernel used with the preprocessing techniques: Kernel Fisher Discriminant Analysis (KFDA) and Kernel Principal Component Analysis (KPCA) is made. The measures of performance used were: Target alignment, Alpha, Beta, Gamma and Fisher. The best results were obtained using the KFDA with the Alpha metric achieving a significant reduction in the dimension of the workspace and a high accuracy in the fault diagnosis. As fault classifier in the Tennessee Eastman Process benchmark an Artificial Neural Network was used. (C) 2015 Elsevier Ltd. All rights reserved.