Nuclear Plant Device Early Warning With Importance-Based Alert Prioritization
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Solution Overview
Problem
Existing early warning systems in power plants generate frequent warnings, requiring significant operator effort to distinguish between important and unimportant alerts, and fail to differentiate between actual device abnormalities and scheduled maintenance or experiments, leading to inefficient operation and potential device breakdowns.
Innovation Solution
A method and system that determine device importance and warning validity by using a pattern learning model to group similar monitoring variables, calculate prediction values, and adjust warning generation frequency based on importance, distinguishing between warnings that require analysis and those that do not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If warning is generated for every monitoring variable exceeding normal operation range, then abnormality detection coverage is improved, but warning frequency increases causing operator overload
Solution Approach 1:
The patent changes the parameter of warning generation from binary (generate/not generate) to a weighted priority system. By introducing device importance levels and warning priority scores, the system transforms the single parameter of warning generation into multiple parameters including device importance, abnormality severity, and warning priority, enabling differentiated warning management that reduces operator overload while maintaining comprehensive monitoring coverage
Solution Approach 2:
The patent segments the monitoring system into multiple priority levels based on device importance. Devices are categorized into different importance levels (e.g., critical, important, general), and warnings are segmented accordingly. This segmentation allows operators to focus on high-priority warnings first, reducing the cognitive load caused by treating all warnings equally while maintaining comprehensive abnormality detection across all device types
2Device complexity
If device importance is not considered in warning generation, then monitoring simplicity is improved, but operator workload increases due to need to determine importance manually
Solution Approach 1:
The patent applies preliminary action by pre-classifying devices into importance levels before warnings are generated. The system automatically determines device importance based on pre-configured device information, operational criticality, and safety implications. This preliminary classification eliminates the need for operators to manually assess warning importance in real-time, reducing their workload while maintaining comprehensive monitoring coverage
Solution Approach 2:
The monitoring system performs self-service by automatically determining device importance and warning priority without requiring operator intervention. The system uses pre-configured device data, operational parameters, and safety criteria to autonomously classify warnings into priority levels, freeing operators from manual importance assessment while ensuring consistent and objective warning prioritization
3Ease of operation
If all warnings are treated equally without distinguishing scheduled maintenance from actual abnormalities, then monitoring uniformity is improved, but warning accuracy decreases requiring analysis of every warning
Solution Approach 1:
The patent introduces an intermediary classification layer between warning generation and operator notification. This intermediary system cross-references warning data with scheduled maintenance plans, experiment schedules, and device operational status to determine whether a warning represents an actual abnormality or a planned activity. By acting as a mediator, the system filters out false alarms from scheduled activities while maintaining uniform monitoring procedures across all devices
Solution Approach 2:
The system implements feedback by continuously comparing generated warnings against scheduled maintenance plans and operational schedules. When a warning is generated, the system automatically checks whether the corresponding device is undergoing scheduled maintenance or experiments, and adjusts the warning status accordingly. This feedback mechanism improves warning accuracy by distinguishing between actual abnormalities and planned activities without requiring operators to manually analyze each warning
Data Source
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AI summary
The present invention provides a method for early warning of an abnormality sign of a device, which includes device importance and warning validity determination, and a system therefor. The method for early warning of the abnormality sign of the device comprises: a first step of determining by an early warning processing apparatus whether a device monitoring signal value exceeds a normal operation range by using a weight value on the basis of monitoring-parameter-specific importance data which has been previously analyzed by an operator; a second step of generating a warning by the early warning processing apparatus when the device monitoring signal value exceeds the normal operation range; and a third step of determining by a warning determination apparatus whether the generated warning is a valid warning, which is subject to a warning analysis and to be traced.