Capturing Causality from Process Data

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

doi:10.1007/978-3-319-05380-6_5

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…A recent review of literature conducted by Jiang and Yan [27] has identified that one of the key challenges in the plant-wide dynamics of the chemical process is the task of cause-effect and oscillation propagation path analysis. Capturing causality and connectivity has been a challenge for complex industrial-scale processes [28]. The demonstration in this work on the use of generalized variance decomposition to determine oscillation connectedness and the rendering of the NPDC volatility index as network graphs are aimed at introducing the said approach as an additional tool detecting and handling plant-wide oscillations caused by disturbances and faults.…”

Section: Significance Of the Current Workmentioning

confidence: 99%

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

Fortela

Mikolajczyk

2023

Processes

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This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to compute the net pairwise dynamic connectedness (NPDC), which originated as a volatility spillover index for financial markets studies in the field of econometrics. This work used the anomaly-detection benchmark Tennessee-Eastman chemical process (TEP) dataset, which consists of 41 measured variables and 11 manipulated variables subjected to various faulty operating conditions. The data analytics was performed using key functions from the R-package ‘ConnectednessApproach’ that implements connectedness computations based on time and frequency. The NPDC coefficient matrices were then transformed into network adjacency matrices for the rendering of the network topology of connectedness for TEP. The resulting network topologies allow a comprehensive analysis of oscillation effects across all plant-measured and manipulated variables. Analyzing the directed connectedness of the system dynamics at short-range, mid-range, and long-range frequencies showed how the oscillation effects of disturbances and faults propagate and dissipate in the short-term, mid-term, and long-term periods.

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Section: Significance Of the Current Workmentioning

confidence: 99%

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

Fortela

Mikolajczyk

2023

Processes

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“…Models play a vital role in understanding these matters, but it is quite difficult to obtain a precise model. In the control and automation community, an underlying model can be obtained by analyzing connectivity and causality, which also have a number of potential applications in the analysis and design of, and fault diagnosis in, large complex industrial processes [1]. In other words, a process topology can be used for risk assessment, root cause analysis, and consequential alarm series identification by using the information of fault propagation [2].…”

Section: Introductionmentioning

confidence: 99%

“…The Bayesian network, a conditional independence method, provides a graph with probabilities [12]. However, its major limitation is that the physical explanation of probabilities is not straightforward, which is unacceptable by engineers sometimes [1]. Granger causality, a dynamic approach, requires a linear regression model [8,13].…”

Section: Introductionmentioning

confidence: 99%

Capturing Causality for Fault Diagnosis Based on Multi-Valued Alarm Series Using Transfer Entropy

Wang

Zhang

et al. 2017

Entropy

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Abstract:Transfer entropy (TE) is a model-free approach based on information theory to capture causality between variables, which has been used for the modeling and monitoring of, and fault diagnosis in, complex industrial processes. It is able to detect the causality between variables without assuming any underlying model, but it is computationally burdensome. To overcome this limitation, a hybrid method of TE and the modified conditional mutual information (CMI) approach is proposed by using generated multi-valued alarm series. In order to obtain a process topology, TE can generate a causal map of all sub-processes and modified CMI can be used to distinguish the direct connectivity from the above-mentioned causal map by using multi-valued alarm series. The effectiveness and accuracy rate of the proposed method are validated by simulated and real industrial cases (the Tennessee-Eastman process) to capture process topology by using multi-valued alarm series.

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“…For example, Duan et al extended the traditional concept of TE and made it more applicable, especially for multivariate cases [16,17]. Yang et al [18] and Duan et al [19] also summarized these methods for capturing causality in industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy

Yang

2015

Entropy

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In modern industrial processes, it is easier and less expensive to configure alarms by software settings rather than by wiring, which causes the rapid growth of the number of alarms. Moreover, because there exist complex interactions, in particular the causal relationship among different parts in the process, a fault may propagate along propagation pathways once an abnormal situation occurs, which brings great difficulty to operators to identify its root cause immediately and to take proper actions correctly. Therefore, causality detection becomes a very important problem in the context of multivariate alarm analysis and design. Transfer entropy has become an effective and widely-used method to detect causality between different continuous process variables in both linear and nonlinear situations in recent years. However, such conventional methods to detect causality based on transfer entropy are computationally costly. Alternatively, using binary alarm series can be more computational-friendly and more direct because alarm data analysis is straightforward for alarm management in practice. The methodology and implementation issues are discussed in this paper. Illustrated by several case studies, including both numerical cases and simulated industrial cases, the proposed method is demonstrated to be suitable for industrial situations contaminated by noise.

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Capturing Causality from Process Data

Cited by 5 publications

References 29 publications

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

Capturing Causality for Fault Diagnosis Based on Multi-Valued Alarm Series Using Transfer Entropy

Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy

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