MPGE and RootRank: A sufficient root cause characterization and quantification framework for industrial process faults. (April 2023)

• Record Type: Journal Article
• Authors: Song, Pengyu; Zhao, Chunhui; Huang, Biao
• Is Part Of: Neural networks. Volume 161(2023)
• Page Start: 397
• Page End: 417
• Publication Date: 2023-04
• Subjects: Root cause diagnosis -- Multi-level predictive graph extraction -- RootRank scoring -- Hierarchical adjacency pruning -- Multi-level Granger causality; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2023.01.030
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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. Highlights: Distinctions between root cause diagnosis and causal inference are analyzed. Both direct and indirect causalities are characterized to highlight root causes. Critical multi-level fault propagation paths can be automatically identified. RootRank can convert complex causal structures into definitive root cause scores.