Description of Connectivity and Causality

Yang, Fan; Duan, Ping; Shah, Sirish L.; Chen, Tongwen

doi:10.1007/978-3-319-05380-6_3

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…In a process system, propagation path for faulty and normal variation is often not the same . The conditional probability in essence should reflect the causal relations between variables under faulty conditions.…”

Section: Methodology: Bn-based Process Monitoring Approachmentioning

confidence: 99%

Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

Gharahbagheri

Imtiaz

Khan

2017

Ind. Eng. Chem. Res.

118

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This paper develops a methodology to combine diagnostic information from various fault detection and isolation tools to diagnose the true root cause of an abnormal event in industrial processes. Limited diagnostic information from kernel principal component analysis, other online fault detection and diagnostic tools, and process knowledge were combined through Bayesian belief network. The proposed methodology will enable an operator to diagnose the root cause of the abnormality. Further, some challenges on application of Bayesian network on process fault diagnosis such as network connection determination, estimation of conditional probabilities, and cyclic loop handling were addressed. The proposed methodology was applied to Fluid Catalytic Cracking unit and Tennessee Eastman Chemical Process. In both cases, the proposed approach showed a good capability of diagnosing the root cause of abnormal conditions.

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Section: Methodology: Bn-based Process Monitoring Approachmentioning

confidence: 99%

Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

Gharahbagheri

Imtiaz

Khan

2017

Ind. Eng. Chem. Res.

118

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“…Causality inference is a process to infer Cause-Effect relations between variables, typically in complex systems, and it is commonly used for root cause analysis in large-scale process industries. A variety of causality analysis techniques have been developed and shown to be effective for root cause diagnosis [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…Existing techniques for causality inference can be generally divided into two types, namely, process knowledge-based methods and data-driven methods [ 1 ]. The former obtains connectivity and causality from prior knowledge, such as process topology and first-principle models, and convert the results into computer accessible formats, such as the adjacency matrix [ 2 ] and signed directed graph [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Cause-Effect Relations Based on Information Granulation and Transfer Entropy

Zhang

Yang

2022

Entropy

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Causality inference is a process to infer Cause-Effect relations between variables in, typically, complex systems, and it is commonly used for root cause analysis in large-scale process industries. Transfer entropy (TE), as a non-parametric causality inference method, is an effective method to detect Cause-Effect relations in both linear and nonlinear processes. However, a major drawback of transfer entropy lies in the high computational complexity, which hinders its real application, especially in systems that have high requirements for real-time estimation. Motivated by such a problem, this study proposes an improved method for causality inference based on transfer entropy and information granulation. The calculation of transfer entropy is improved with a new framework that integrates the information granulation as a critical preceding step; moreover, a window-length determination method is proposed based on delay estimation, so as to conduct appropriate data compression using information granulation. The effectiveness of the proposed method is demonstrated by both a numerical example and an industrial case, with a two-tank simulation model. As shown by the results, the proposed method can reduce the computational complexity significantly while holding a strong capability for accurate casuality detection.

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“…Complex industrial processes are interconnected by different units or elements, which are coupled and operate in conjunction with each other . When faults occur, the abnormality can easily propagate between process variables and trigger massive alarms, which would overwhelm the operators.…”

Section: Introductionmentioning

confidence: 99%

Single Model-Based Analysis of Relative Causal Changes for Root-Cause Diagnosis in Complex Industrial Processes

Tian

Zhao

2021

Ind. Eng. Chem. Res.

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Traditional root-cause diagnosis methods analyze different faults independently, suffering from repetitive modeling efforts for each fault. Besides, they are disturbed by significant redundancy and interference relations, being both time and effort consuming in online applications. In this work, a novel strategy is proposed to diagnose root cause by analyzing the changes in causal effects between the normal status and fault statuses, which allows a single causal model to diagnose different faults. The enhanced cross mapping (ECM) is proposed to carry out the analysis of the causal effects, which could detect precise causality and handle interference relations in complex industrial processes. In our strategy, a causal model is constructed from coarse-grained to fine-grained via the ECM to remove interference relations and achieve a concise structure. Besides, causal intensity and direction information of normal status are comprehensively described in the model. By updating causal effects under fault statuses, the relative causal changes from normal to fault can be analyzed and the significant changes are considered responsible for the fault propagation. Consequently, the fault propagation path is highlighted from the causal model and the root cause can be identified along the path. The feasibility of the proposed strategy is demonstrated using both simulation and practical industrial case studies.

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Description of Connectivity and Causality

Cited by 4 publications

References 7 publications

Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network

Detection of Cause-Effect Relations Based on Information Granulation and Transfer Entropy

Single Model-Based Analysis of Relative Causal Changes for Root-Cause Diagnosis in Complex Industrial Processes

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