Quality-Related Root Cause Diagnosis Based on Orthogonal Kernel Principal Component Regression and Transfer Entropy

Abstract

This article is devoted to solving the problem of quality-related root cause diagnosis for nonlinear process. First, an orthogonal kernel principal component regression model is constructed to achieve orthogonal decomposition of feature space, such that quality-related and quality-unrelated faults can be separately detected in the subspaces of opposite correlations to the output, without any effect on each other. Then, in view of the high complexity of traditional nonlinear fault diagnosis methods, an efficient method of kernel sample equivalence replacement is established to replace the partial differential operations of the kernel gradient algorithm, which can convert nonlinear fault detection indicators into the standard quadratic forms of the original variable sample, thereby making it possible to solve the nonlinear fault diagnosis problem by linear manners. Furthermore, a transfer entropy algorithm is utilized to the new model to analyze the causality between the diagnosed candidate faulty variables to find out the accurate root cause of the fault. Finally, comparative studies between the latest result and the proposed one are carried out in the Tennessee Eastman process to verify the effectiveness and superiority of the new method.