A Novel Approach to Alarm Causality Analysis Using Active Dynamic Transfer Entropy

Luo, Yimo; Gopaluni, R. Bhushan; Xu, Yuan; Cao, Liang; Zhu, Qunxiong

doi:10.1021/acs.iecr.9b06262

Cited by 20 publications

(4 citation statements)

References 35 publications

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“…One is to develop novel algorithms that require fewer assumptions. For example, ref can partially handle the problem of time delay, and ref can deal with the problem of nonlinearity. The other one is to integrate process knowledge.…”

Section: Discussion: Challenges and Opportunitiesmentioning

confidence: 99%

Causal Discovery Based on Observational Data and Process Knowledge in Industrial Processes

Cao

et al. 2022

Ind. Eng. Chem. Res.

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Causal discovery approaches are gaining popularity in industrial processes. Existing causal discovery algorithms can indeed find some important causal relationships from industrial data, but, at the same time, the algorithms may also give some incorrect causal relationships. In order to deal with this problem, we give four kinds of process knowledge definitions according to the special characteristics of complex industrial processes. Causal discovery algorithms will yield more accurate results and deeper insights if the process knowledge is properly addressed. Based on commercial-scale fluid catalytic cracker unit data, we validate the effectiveness of the proposed methods with some state-of-the-art causal discovery algorithms.

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Section: Discussion: Challenges and Opportunitiesmentioning

confidence: 99%

Causal Discovery Based on Observational Data and Process Knowledge in Industrial Processes

Cao

et al. 2022

Ind. Eng. Chem. Res.

Self Cite

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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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“…In the field of alarm root cause analysis, TE was adpated to analyze Cause-Effect relations among binary-valued alarm variables [ 24 ]; moreover, a Bayesian network based on active dynamic transfer entropy (ADTE) was proposed to establish an accurate alarm propagation network during an alarm flood [ 25 ]. For oscillation diagnosis, a workflow using TE was proposed to provide a robust procedure for accurately identifying the oscillation propagation path [ 26 ].…”

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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A Novel Approach to Alarm Causality Analysis Using Active Dynamic Transfer Entropy

Cited by 20 publications

References 35 publications

Causal Discovery Based on Observational Data and Process Knowledge in Industrial Processes

Causal Discovery Based on Observational Data and Process Knowledge in Industrial Processes

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

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

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