Identifying Causality from Alarm Observations

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Abstract

The complexity of modern industrial plants poses significant challenges for the design of effective alarm systems. Rigorous alarm management is recommended to ensure that the operators get useful information from the alarm system, rather than being overloaded with irrelevant state information. Alarm management practices have been shown to significantly reduce the frequency of alarms in industrial process plants. These practices help focusing the operators’ attention on actually critical situations. However, they cannot resolve the cascades of critical situations frequently occurring during emergency situations. Multilevel flow modelling (MFM) has been proposed as a way of representing knowledge about the industrial process and infer causes and consequences of deviations throughout the system. The method enables the identification of causes and consequences of alarm situations based on an abstracted model of the mass and energy flows in the system. The application of MFM for root cause analysis based alarm grouping has been demonstrated and can be extended to reason about the direction of causality considering the entirety of the alarms present in the system for more comprehensive decision support. This contribution presents the foundation for combining the cause and consequence propagation of multiple observations from the system based on an MFM model. The proposed logical reasoning matches actually observed alarms to the propagation analysis in MFM to distinguish plausible causes and consequences. This extended analysis results in causal paths from likely root causes to tentative consequences, providing the operator with a comprehensive tool to not only identify but also rank the criticality of a large number of concurrent alarms in the system.
Original languageEnglish
Publication date2017
Number of pages6
Publication statusPublished - 2017
EventInternational Symposium on Future Instrumentation & Control for Nuclear Power Plants - Gyeongju, Korea, Republic of
Duration: 24 Nov 201730 Nov 2017
http://www.isofic.org/

Conference

ConferenceInternational Symposium on Future Instrumentation & Control for Nuclear Power Plants
CountryKorea, Republic of
CityGyeongju
Period24/11/201730/11/2017
Internet address

Keywords

  • Decision support
  • Causality
  • Multilevel flow modelling

Cite this

Kirchhübel, D., Zhang, X., Lind, M., & Ravn, O. (2017). Identifying Causality from Alarm Observations. Paper presented at International Symposium on Future Instrumentation & Control for Nuclear Power Plants, Gyeongju, Korea, Republic of.
Kirchhübel, Denis ; Zhang, Xinxin ; Lind, Morten ; Ravn, Ole. / Identifying Causality from Alarm Observations. Paper presented at International Symposium on Future Instrumentation & Control for Nuclear Power Plants, Gyeongju, Korea, Republic of.6 p.
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abstract = "The complexity of modern industrial plants poses significant challenges for the design of effective alarm systems. Rigorous alarm management is recommended to ensure that the operators get useful information from the alarm system, rather than being overloaded with irrelevant state information. Alarm management practices have been shown to significantly reduce the frequency of alarms in industrial process plants. These practices help focusing the operators’ attention on actually critical situations. However, they cannot resolve the cascades of critical situations frequently occurring during emergency situations. Multilevel flow modelling (MFM) has been proposed as a way of representing knowledge about the industrial process and infer causes and consequences of deviations throughout the system. The method enables the identification of causes and consequences of alarm situations based on an abstracted model of the mass and energy flows in the system. The application of MFM for root cause analysis based alarm grouping has been demonstrated and can be extended to reason about the direction of causality considering the entirety of the alarms present in the system for more comprehensive decision support. This contribution presents the foundation for combining the cause and consequence propagation of multiple observations from the system based on an MFM model. The proposed logical reasoning matches actually observed alarms to the propagation analysis in MFM to distinguish plausible causes and consequences. This extended analysis results in causal paths from likely root causes to tentative consequences, providing the operator with a comprehensive tool to not only identify but also rank the criticality of a large number of concurrent alarms in the system.",
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Kirchhübel, D, Zhang, X, Lind, M & Ravn, O 2017, 'Identifying Causality from Alarm Observations' Paper presented at, Gyeongju, Korea, Republic of, 24/11/2017 - 30/11/2017, .

Identifying Causality from Alarm Observations. / Kirchhübel, Denis; Zhang, Xinxin; Lind, Morten; Ravn, Ole.

2017. Paper presented at International Symposium on Future Instrumentation & Control for Nuclear Power Plants, Gyeongju, Korea, Republic of.

Research output: Contribution to conferencePaperResearchpeer-review

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T1 - Identifying Causality from Alarm Observations

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AU - Zhang, Xinxin

AU - Lind, Morten

AU - Ravn, Ole

PY - 2017

Y1 - 2017

N2 - The complexity of modern industrial plants poses significant challenges for the design of effective alarm systems. Rigorous alarm management is recommended to ensure that the operators get useful information from the alarm system, rather than being overloaded with irrelevant state information. Alarm management practices have been shown to significantly reduce the frequency of alarms in industrial process plants. These practices help focusing the operators’ attention on actually critical situations. However, they cannot resolve the cascades of critical situations frequently occurring during emergency situations. Multilevel flow modelling (MFM) has been proposed as a way of representing knowledge about the industrial process and infer causes and consequences of deviations throughout the system. The method enables the identification of causes and consequences of alarm situations based on an abstracted model of the mass and energy flows in the system. The application of MFM for root cause analysis based alarm grouping has been demonstrated and can be extended to reason about the direction of causality considering the entirety of the alarms present in the system for more comprehensive decision support. This contribution presents the foundation for combining the cause and consequence propagation of multiple observations from the system based on an MFM model. The proposed logical reasoning matches actually observed alarms to the propagation analysis in MFM to distinguish plausible causes and consequences. This extended analysis results in causal paths from likely root causes to tentative consequences, providing the operator with a comprehensive tool to not only identify but also rank the criticality of a large number of concurrent alarms in the system.

AB - The complexity of modern industrial plants poses significant challenges for the design of effective alarm systems. Rigorous alarm management is recommended to ensure that the operators get useful information from the alarm system, rather than being overloaded with irrelevant state information. Alarm management practices have been shown to significantly reduce the frequency of alarms in industrial process plants. These practices help focusing the operators’ attention on actually critical situations. However, they cannot resolve the cascades of critical situations frequently occurring during emergency situations. Multilevel flow modelling (MFM) has been proposed as a way of representing knowledge about the industrial process and infer causes and consequences of deviations throughout the system. The method enables the identification of causes and consequences of alarm situations based on an abstracted model of the mass and energy flows in the system. The application of MFM for root cause analysis based alarm grouping has been demonstrated and can be extended to reason about the direction of causality considering the entirety of the alarms present in the system for more comprehensive decision support. This contribution presents the foundation for combining the cause and consequence propagation of multiple observations from the system based on an MFM model. The proposed logical reasoning matches actually observed alarms to the propagation analysis in MFM to distinguish plausible causes and consequences. This extended analysis results in causal paths from likely root causes to tentative consequences, providing the operator with a comprehensive tool to not only identify but also rank the criticality of a large number of concurrent alarms in the system.

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Kirchhübel D, Zhang X, Lind M, Ravn O. Identifying Causality from Alarm Observations. 2017. Paper presented at International Symposium on Future Instrumentation & Control for Nuclear Power Plants, Gyeongju, Korea, Republic of.