Fault diagnosis based on GMM of KCVA for chemical process

Usually, there are multiple operate modes in chemical process because of producing kinds of product. traditional fault diagnosis methods for single operate mode are no longer applicable when they used to diagnose process of multiple operate models, therefore, This paper proposes a algorithm of kernel canonical variate analysis based on Gaussian Mixture Model, first of all, history data of chemical process is decomposed to multiple Gaussian components by using Gaussian Mixture Model (GMM), then using kernel canonical variate analysis(KCVA) algorithm to model for each Gaussian component, calculating the corresponding statistics for process monitoring. In the TE process simulation, comparison with KCVA algorithm, fault diagnosis result illustrate the effectiveness of the proposed algorithm in this paper.