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Intelligent Alarm Analysis for Fault Diagnosis and Management in Nuclear Power Plants.

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Title: Intelligent Alarm Analysis for Fault Diagnosis and Management in Nuclear Power Plants.
Authors: Li, Wei1,2 (AUTHOR), Yang, Kunze2,3 (AUTHOR) kby5080@psu.edu, Yang, Ming1,3,4 (AUTHOR), Wang, Jipu4 (AUTHOR), Chen, Sijuan1,4 (AUTHOR) sijuanchen@szu.edu.cn
Source: Energies (19961073). Apr2025, Vol. 18 Issue 7, p1730. 22p.
Subjects: Model-based reasoning, Fault diagnosis, Power plant management, Multilevel models, Root cause analysis, Alarms
Abstract: The safety and efficiency of nuclear power plants (NPPs) are heavily reliant on the timely and accurate decisions made by operators in the control room, particularly during fault scenarios. Traditional alarm systems often fall short in identifying the root causes of faults, especially in complex, multi-fault situations. This paper presents an advanced fault diagnosis method based on the multilevel flow model (MFM), which addresses the limitations of conventional alarm systems by linking alarms to system goals and functions. The MFM framework organizes the system hierarchically, aiding in the clear understanding of causal relationships and the underlying causes of faults. Additionally, this paper proposes a method that combines the longest causal path and root cause analysis, addressing both single- and multiple-fault diagnosis needs. This approach improves diagnostic efficiency and comprehensiveness, providing operators with a systematic basis for analysis and decision-making in multi-alarm scenarios. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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PubType: Academic Journal
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  Data: Intelligent Alarm Analysis for Fault Diagnosis and Management in Nuclear Power Plants.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Wei%22">Li, Wei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Kunze%22">Yang, Kunze</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> kby5080@psu.edu</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Ming%22">Yang, Ming</searchLink><relatesTo>1,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jipu%22">Wang, Jipu</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Sijuan%22">Chen, Sijuan</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> sijuanchen@szu.edu.cn</i>
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  Data: The safety and efficiency of nuclear power plants (NPPs) are heavily reliant on the timely and accurate decisions made by operators in the control room, particularly during fault scenarios. Traditional alarm systems often fall short in identifying the root causes of faults, especially in complex, multi-fault situations. This paper presents an advanced fault diagnosis method based on the multilevel flow model (MFM), which addresses the limitations of conventional alarm systems by linking alarms to system goals and functions. The MFM framework organizes the system hierarchically, aiding in the clear understanding of causal relationships and the underlying causes of faults. Additionally, this paper proposes a method that combines the longest causal path and root cause analysis, addressing both single- and multiple-fault diagnosis needs. This approach improves diagnostic efficiency and comprehensiveness, providing operators with a systematic basis for analysis and decision-making in multi-alarm scenarios. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/en18071730
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      – Code: eng
        Text: English
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      – SubjectFull: Power plant management
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      – SubjectFull: Multilevel models
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      – SubjectFull: Root cause analysis
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      – SubjectFull: Alarms
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              Text: Apr2025
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