Root cause diagnosis for process faults based on multisensor time-series causality discovery

Wang, Sheng; Zhao, Qiang; Han, Yinghua; Wang, Jinkuan

doi:10.1016/j.jprocont.2022.12.006

Cited by 11 publications

(1 citation statement)

References 44 publications

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“…Once the operator is misled by the analysis results, it will lead to irreversible accidents. Recently, some scholars [19], [20], [21] have gradually realized this problem, but their focus is on the time delay between different variables, and they have not discussed its variability in depth, so the results are undoubtedly incomplete.…”

Section: Introductionmentioning

confidence: 99%

Variable Time Delay-Based Granger Causality Approach Integrated with Dynamic Coupling Analysis for Root Cause Diagnosis in Chemical Processes

Li,

Yang,

Huang

et al. 2024

Korean J. Chem. Eng.

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Due to the dynamic characteristics of chemical industry systems, the time delay tends to be variable, which leads to changes in coupling intensity. This is contrary to the assumptions in causal analysis, where the time delay and coupling are typically assumed to be fixed. In this article, a new causal analysis framework that integrates Granger causality with dynamic coupling analysis based on variable time delay is proposed, which not only fully considers the variable time delay in dynamic processes, but also studies the dynamic change of coupling intensity. Firstly, the moving window is used to explore real-time variations in average mutual information to obtain the variable time delay. Then by further analyzing the normal data, the dynamic coupling relationship caused by continuous changes in time delay is distinguished. On this basis, the extended Granger causality and convergent cross mapping are integrated to relax their assumptions of fixed time delay and coupling. Finally, the direction of fault propagation is guided by the results of causal analysis. The effectiveness of proposed mothed is demonstrated by chemical industry case studies.

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Section: Introductionmentioning

confidence: 99%