A method for detecting causal relationships between industrial alarm variables using Transfer Entropy and K2 algorithm

Abreu, Rute Souza de; Nunes, Yuri Thomas; Guedes, Luiz Affonso; Silva, Ivanovitch

doi:10.1016/j.jprocont.2021.09.001

Cited by 16 publications

(3 citation statements)

References 34 publications

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“…Here, an alarm threshold of six standard deviations is considered to attain a low false alarm rate. The alarm states are recorded every 3 min for the next 40 h. Although both the process variable data and alarm data can be utilized for diagnosis, the use of alarm data has been more prominent in this field in recent years. − Therefore, alarm data is utilized in this case study. The MATLAB R2018a computational environment is used to carry out the simulations for the data generation.…”

Section: Resultsmentioning

confidence: 99%

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

Kumari

Wang

Khan

et al. 2022

Ind. Eng. Chem. Res.

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In chemical processes, Bayesian network (BN)-based approaches have been extensively applied for process fault diagnosis. Generally, BN is learned using score and search algorithms where search algorithms create candidate networks whose fitness to data is measured by scores. However, existing approaches cannot utilize cyclic loop knowledge while learning BN. Since cyclic loops are prevalent in chemical processes, their unaccountability results in inaccurate BN and reduces diagnosis accuracy. Therefore, for accurate diagnosis, we propose direct transfer entropy (DTE)-based multiblock BN to discover cyclic loops while learning BN. First, the process is segmented into multiple blocks. Next, block-level BNs are learned using DTE-based score and Greedy search. By eliminating the common source variable effect, DTE finds correct causality between process variables and obtains accurate block-level BNs, which are fused to discover significant cyclic loops that were otherwise unachievable when finding BN. The performance of the developed methodology is demonstrated through a benchmark process.

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Section: Resultsmentioning

confidence: 99%

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

Kumari

Wang

Khan

et al. 2022

Ind. Eng. Chem. Res.

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“…In causal inference of time series, correlation does not equal causation, For example, Ma Jian [15] modeled and analyzed the meteorological data in Beijing, and verified that there may be zero correlation and strong causality between variables. The transfer entropy derived from information theory can deal with complex nonlinear causal relationships and has achieved remarkable results in the fields of industry [16] , finance, and brain science [17] . Kim et al proposed the transfer entropy-based method TENET [18] .…”

Section: Introductionmentioning

confidence: 99%

A new gene regulatory network reconstruction method based on single cell sequencing and transfer entropy

Zhu

Gao

Liu

2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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In systems biology, gene regulatory networks can well reveal complex biological systems and dynamic biological processes. Traditional gene regulatory network reconstruction methods ignore dynamic biological processes. A gene regulatory network reconstruction method based on transfer entropy and single-cell dynamic time series data is proposed to solve this problem. And from a theoretical point of view, the possibility and rationality of transfer entropy in the application of big data and causal reasoning are proved in detail. Firstly, the method construct pseudo-time series dynamic gene expression data based on trajectory inference. Secondly, The transfer entropy is used to calculate the directional transfer information between paired genes, and screen the major gene regulatory relationships . Finally, remove the indirect gene regulation relationship according to the data processing inequality, and construct the gene regulation network . This method takes the single cell sequencing data of early mouse embryo blood development as an example, and selects TENET and DynGENIE3 algorithms as comparisons, which proves the feasibility and effectiveness of this method from the theoretical and experimental perspectives. The experimental results not only identify the key cell development regulatory factors and regulatory relationships, but also consume less time. It can not only provide important reference value and assumptions for biological disturbance experiment, but also reduce the time cost and research cost of biological experiment.

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“…In [14,15], transfer entropies were exploited and modified to detect the causal relations between alarm signals. An active dynamic transfer entropy was proposed in [18] and a K2-algorithm-based transfer entropy approach was proposed in [19] to conduct alarm causality analysis. In addition, data mining approaches are effective in extraction of interesting association rules and sequential patterns, and have been applied to find temporal dependencies between alarms from historical alarm and event data [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis

Li,

Cheng

2023

Entropy

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Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

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A method for detecting causal relationships between industrial alarm variables using Transfer Entropy and K2 algorithm

Cited by 16 publications

References 34 publications

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

A new gene regulatory network reconstruction method based on single cell sequencing and transfer entropy

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis

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