Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed: (1)the adjacency matrix is determined by partial correlation coefficient; (2)the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram; (3)interpretative structural for large-scale process control system is built by this ISM method; and (4)non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.

Graphic abstractThe HOS–ISM fault diagnosis framework integrates HOS and ISM and combines a priori process with data-driven methods to determine control loop, increase the accuracy of data selection, and cover the shortage of uncertainty in diagnosis result of HOS. The procedure of the method is as follows: (1)The adjacency matrix is determined by partial correlation coefficient; (2)the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram; (3)interpretative structural for large-scale process control system is built by the ISM method;and (4)non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of nonlinear characteristic diagnostic method with reasonable sampling period and data window.Figure optionsDownload full-size imageDownload as PowerPoint slide