Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to ex...Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of alarms.Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses.Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data.To improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms.The topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process.The root cause is then obtained from the directed acyclic graph(DAG)among alarm variables.The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.展开更多
Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and...Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.展开更多
Purpose:With the availability of large-scale scholarly datasets,scientists from various domains hope to understand the underlying mechanisms behind science,forming a vibrant area of inquiry in the emerging“science of...Purpose:With the availability of large-scale scholarly datasets,scientists from various domains hope to understand the underlying mechanisms behind science,forming a vibrant area of inquiry in the emerging“science of science”field.As the results from the science of science often has strong policy implications,understanding the causal relationships between variables becomes prominent.However,the most credible quasi-experimental method among all causal inference methods,and a highly valuable tool in the empirical toolkit,Regression Discontinuity Design(RDD)has not been fully exploited in the field of science of science.In this paper,we provide a systematic survey of the RDD method,and its practical applications in the science of science.Design/methodology/approach:First,we introduce the basic assumptions,mathematical notations,and two types of RDD,i.e.,sharp and fuzzy RDD.Second,we use the Web of Science and the Microsoft Academic Graph datasets to study the evolution and citation patterns of RDD papers.Moreover,we provide a systematic survey of the applications of RDD methodologies in various scientific domains,as well as in the science of science.Finally,we demonstrate a case study to estimate the effect of Head Start Funding Proposals on child mortality.Findings:RDD was almost neglected for 30 years after it was first introduced in 1960.Afterward,scientists used mathematical and economic tools to develop the RDD methodology.After 2010,RDD methods showed strong applications in various domains,including medicine,psychology,political science and environmental science.However,we also notice that the RDD method has not been well developed in science of science research.Research Limitations:This work uses a keyword search to obtain RDD papers,which may neglect some related work.Additionally,our work does not aim to develop rigorous mathematical and technical details of RDD but rather focuses on its intuitions and applications.Practical implications:This work proposes how to use the RDD method in science of science research.Originality/value:This work systematically introduces the RDD,and calls for the awareness of using such a method in the field of science of science.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61903345 and 61973287)。
文摘Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of alarms.Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses.Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data.To improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms.The topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process.The root cause is then obtained from the directed acyclic graph(DAG)among alarm variables.The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.
基金This research was funded by the National Natural Science Foundation of China(Grant No.72074060).
文摘Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.
基金This work was supported by grants from the National Natural Science Foundation of China under Grant Nos.72004177 and L1924078.
文摘Purpose:With the availability of large-scale scholarly datasets,scientists from various domains hope to understand the underlying mechanisms behind science,forming a vibrant area of inquiry in the emerging“science of science”field.As the results from the science of science often has strong policy implications,understanding the causal relationships between variables becomes prominent.However,the most credible quasi-experimental method among all causal inference methods,and a highly valuable tool in the empirical toolkit,Regression Discontinuity Design(RDD)has not been fully exploited in the field of science of science.In this paper,we provide a systematic survey of the RDD method,and its practical applications in the science of science.Design/methodology/approach:First,we introduce the basic assumptions,mathematical notations,and two types of RDD,i.e.,sharp and fuzzy RDD.Second,we use the Web of Science and the Microsoft Academic Graph datasets to study the evolution and citation patterns of RDD papers.Moreover,we provide a systematic survey of the applications of RDD methodologies in various scientific domains,as well as in the science of science.Finally,we demonstrate a case study to estimate the effect of Head Start Funding Proposals on child mortality.Findings:RDD was almost neglected for 30 years after it was first introduced in 1960.Afterward,scientists used mathematical and economic tools to develop the RDD methodology.After 2010,RDD methods showed strong applications in various domains,including medicine,psychology,political science and environmental science.However,we also notice that the RDD method has not been well developed in science of science research.Research Limitations:This work uses a keyword search to obtain RDD papers,which may neglect some related work.Additionally,our work does not aim to develop rigorous mathematical and technical details of RDD but rather focuses on its intuitions and applications.Practical implications:This work proposes how to use the RDD method in science of science research.Originality/value:This work systematically introduces the RDD,and calls for the awareness of using such a method in the field of science of science.
