Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalizati...Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.展开更多
Likert scales are a common methodological tool for data collection used in quantitative or mixed-method approaches in multiple domains.They are often employed in surveys or questionnaires,for benchmarking answers in t...Likert scales are a common methodological tool for data collection used in quantitative or mixed-method approaches in multiple domains.They are often employed in surveys or questionnaires,for benchmarking answers in the fields of disaster risk reduction,business continuity management,and organizational resilience.However,both scholars and practitioners may lack a simple scale of reference to assure consistency across disciplinary fields.This article introduces a simple-to-use rating tool that can be used for benchmarking responses in questionnaires,for example,for assessing disaster risk reduction,gaps in operational capacity,and organizational resilience.We aim,in particular,to support applications in contexts in which the target groups,due to cultural,social,or political reasons,may be unsuitable for in-depth analyses that use,for example,scales from 1 to 7 or from 1 to 10.This methodology is derived from the needs emerged in our recent fieldwork on interdisciplinary projects and from dialogue with the stakeholders involved.The output is a replicable scale from 0 to 3 presented in a table that includes category labels with qualitative attributes and descriptive equivalents to be used in the formulation of model answers.These include examples of levels of resilience,capacity,and gaps.They are connected to other tools that could be used for in-depth analysis.The advantage of our Likert scale-based response model is that it can be applied in a wide variety of disciplines,from social science to engineering.展开更多
基金Supported by the National Natural Science Foundation of China(61473026,61104131)the Fundamental Research Funds for the Central Universities(JD1413)
文摘Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.
基金BA/Leverhulme Small Research Grant Award 2019 supported by the United Kingdom’s Department for Business,Energy and Industrial Strategy(Grant Reference:SRG19/191797)the Earthquake Engineering Field Investigation Team Award 2019 by the Institution of Structural Engineers in the UK+2 种基金the Mexican Consejo Nacional de Ciencia y Tecnología(Grant Reference:398485)the European Union’s Horizon 2020 Research and Innovation Programme(Grant Agreement:821046)the TURNkey(Towards more Earthquakeresilient Urban Societies through a Multi-sensor-based Information System enabling Earthquake Forecasting,Early Warning and Rapid Response actions)Project。
文摘Likert scales are a common methodological tool for data collection used in quantitative or mixed-method approaches in multiple domains.They are often employed in surveys or questionnaires,for benchmarking answers in the fields of disaster risk reduction,business continuity management,and organizational resilience.However,both scholars and practitioners may lack a simple scale of reference to assure consistency across disciplinary fields.This article introduces a simple-to-use rating tool that can be used for benchmarking responses in questionnaires,for example,for assessing disaster risk reduction,gaps in operational capacity,and organizational resilience.We aim,in particular,to support applications in contexts in which the target groups,due to cultural,social,or political reasons,may be unsuitable for in-depth analyses that use,for example,scales from 1 to 7 or from 1 to 10.This methodology is derived from the needs emerged in our recent fieldwork on interdisciplinary projects and from dialogue with the stakeholders involved.The output is a replicable scale from 0 to 3 presented in a table that includes category labels with qualitative attributes and descriptive equivalents to be used in the formulation of model answers.These include examples of levels of resilience,capacity,and gaps.They are connected to other tools that could be used for in-depth analysis.The advantage of our Likert scale-based response model is that it can be applied in a wide variety of disciplines,from social science to engineering.
