Fault Detection and Root Cause Analysis of a Batch Process via Novel Nonlinear Dissimilarity and Comparative Granger Causality Analysis

MPGE and RootRank: A sufficient root cause characterization and quantification framework for industrial process faults

Root cause diagnosis can locate abnormalities of industrial processes, ensuring production safety and manufacturing efficiency. However, existing root cause diagnosis models only consider pairwise direct causality and ignore the multi-level fault propagation, which may lead to incomplete root cause descriptions and ambiguous root cause candidates. To address the above issue, a novel framework, named multi-level predictive graph extraction (MPGE) and RootRank scoring, is proposed and applied to the root cause diagnosis for industrial processes. In this framework, both direct and indirect Granger causalities are characterized by multi-level predictive relationships to provide a sufficient characterization of root cause variables. First, a predictive graph structure with a sparse constrained adjacency matrix is constructed to describe the information transmission between variables. The information of variables is deeply fused according to the adjacency matrix to consider multi-level fault propagation. Then, a hierarchical adjacency pruning (HAP) mechanism is designed to automatically capture vital predictive relationships through adjacency redistribution. In this way, the multi-level causalities between variables are extracted to fully describe both direct and indirect fault propagation and highlight the root cause. Further, a RootRank scoring algorithm is proposed to analyze the predictive graph and quantify the fault propagation contribution of each variable, thereby giving definite root cause identification results. Three examples are adopted to verify the diagnostic performance of the proposed framework, including a numerical example, the Tennessee Eastman benchmark process, and a real cut-made process of cigarette. Both theoretical analysis and experimental verification show the high interpretability and reliability of the proposed framework.

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

This study considers the problem of distinguishing between process and sensor faults in nonlinear chemical processes. An integrated fault diagnosis framework is proposed to distinguish chemical process sensor faults from process faults. The key idea of the framework is to embed the cycle temporal algorithm into the dynamic kernel principal component analysis to improve the fault detection speed and accuracy. It is combined with the fault diagnosis method based on the reconstruction-based contribution graph to diagnose the fault variables and then distinguish the two fault types according to their characteristics. Finally, the integrated fault diagnosis framework is applied to the Tennessee Eastman process and acid gas absorption process, and its effectiveness is proved.