Semiparametric Principal Component Analysishas advantages over Principal Component Analysis(PCA), as it can deal with nonlinear and non-monotonic correlation and non-Gaussian distribution process data. In Semiparametric PCA the distance correlation coefficient matrix is used to replace the covariance matrix, and asemi-parametric Gaussian transformationis usedto allow variables to follow multivariate Gaussian distribution. To reduce the cost of monitoringand alarm flooding,afault diagnosis technique, which combines Semiparametric PCA and Bayesian Network (BN),isproposedhere. In the first stage, Semiparametric PCA is used to find the faultinmonitored variables. And considering the interaction of process variables and historical process data,a Bayesian network is developed inthesecond stage. Considering Semiparametric PCA outcome as evidence, the Bayesian network applies deductive and abductive reasoningto update and analysis, whichassist in determiningthe true rootcause(s)and fault propagation pathway. The implementation and applicability of the proposed methodology are demonstrated using three process systems. This article is protected by copyright. All rights reserved.
