2 research outputs found
Fault Detection and Diagnosis for Nonlinear and Non-Gaussian Processes Based on Copula Subspace Division
A novel copula subspace division
strategy is proposed for fault
detection and diagnosis. High-dimensional industrial data are analyzed
in two elemental subspaces: margin distribution subspace (MDS) modeled
by joint margin distribution, and dependence structure subspace (DSS)
modeled by copula. The highest density regions of two submodels are
introduced and quantified using probability indices. To improve the
robustness of the monitoring index, a hyperrectangular control boundary
in MDS is designed, and the equivalent univariate control limits are
estimated. Two associated contribution indices are also constructed
for fault diagnosis. The interactive relationships among the root-cause
variables are investigated via a proposed state chart. The effectiveness
and superiority of the proposed approaches (double-subspace and multisubspace)
are validated using a numerical example and the Tennessee Eastman
chemical process. Better monitoring performance is achieved compared
with some conventional approaches such as principal component analysis,
independent component analysis, kernel principal component analysis
and vine copula-based dependence description. The proposed multisubspace
approach fully utilizes univariate-based alarm data with a dependence
restriction modulus, which is promising for industrial application
Fault Detection and Diagnosis for Nonlinear and Non-Gaussian Processes Based on Copula Subspace Division
A novel copula subspace division
strategy is proposed for fault
detection and diagnosis. High-dimensional industrial data are analyzed
in two elemental subspaces: margin distribution subspace (MDS) modeled
by joint margin distribution, and dependence structure subspace (DSS)
modeled by copula. The highest density regions of two submodels are
introduced and quantified using probability indices. To improve the
robustness of the monitoring index, a hyperrectangular control boundary
in MDS is designed, and the equivalent univariate control limits are
estimated. Two associated contribution indices are also constructed
for fault diagnosis. The interactive relationships among the root-cause
variables are investigated via a proposed state chart. The effectiveness
and superiority of the proposed approaches (double-subspace and multisubspace)
are validated using a numerical example and the Tennessee Eastman
chemical process. Better monitoring performance is achieved compared
with some conventional approaches such as principal component analysis,
independent component analysis, kernel principal component analysis
and vine copula-based dependence description. The proposed multisubspace
approach fully utilizes univariate-based alarm data with a dependence
restriction modulus, which is promising for industrial application