Process Fault Detection Based on MCFS-R-Vine Copula
The R-Vine Copula model has progressively gained importance in the process monitoring of chemical industry because of its good ability to characterize complex high-dimensional variable interrelationships.In previous applications of R-Vine Copula model,two approaches of data variables processing were used:dimensionality reduction or no dimensionality reduction.However,it could destroy the data structure or increase the cost of R-Vine Copula modeling.Based on these problems,a method called MCFS-RVC,which combines Multi Class Feature Selec-tion(MCFS),is proposed to preserve data structures and reduce the cost of constructing R-Vine Copula models,and it is applied in the field of chemical process monitoring.The values of probability density functions obtained from the R-Vine Copula model were log-transformed,and it produced the corresponding probability density index poutliers through kernel density estimation theory to achieve a real time nonlinear non-gaussian process fault detection.The monitoring results of the Tennessee Eastman(TE)process shows that the proposed MCFS-RVC approach achieves good performance in chemical process fault monitoring.
Process monitoringFault detectionMultiple class feature selectionKernel density estimation
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