Plant Alarm Apparatus for Early Abnormality Detection
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Solution Overview
Problem
Conventional power plant monitoring systems require skilled operators to define and repeatedly adjust upper and lower limit values for detecting abnormalities, which is time-consuming and labor-intensive, and may not provide timely alerts for potential issues.
Innovation Solution
A plant alarm apparatus and method that periodically collects data from sensors, stores it in a latest plant data value table, and compares it with predefined alarm-triggering conditions to detect deviations, issuing preliminary alerts before actual abnormalities occur, allowing for early operator notification and minimizing damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If skilled operators manually define and adjust upper and lower limit values for abnormality detection, then the system can detect plant abnormalities, but the process becomes time-consuming and labor-intensive requiring repeated test runs and adjustments
Solution Approach 1:
The system performs preliminary analysis by comparing current sensor data with historical data to automatically predict abnormality occurrence. This preliminary action eliminates the need for manual limit value definition and repeated adjustments, as the system proactively identifies trends indicating potential abnormalities before they occur.
Solution Approach 2:
The abnormality detection system serves itself by automatically learning from historical plant data and autonomously identifying patterns that precede abnormalities. The system updates its detection criteria based on accumulated data without requiring skilled operators to manually adjust parameters, enabling self-improving detection capability.
2Reliability
If conventional limit value comparison methods are used, then plant abnormalities can be detected, but operator response time is delayed requiring abundant knowledge and experience
Solution Approach 1:
The system performs preliminary detection by analyzing trends in sensor data against historical patterns to identify foretokens of abnormality before they manifest as actual abnormalities. This advance detection provides operators with early warning, enabling timely response without requiring deep expertise in interpreting complex sensor data.
Solution Approach 2:
The system continuously compares current plant data with historical data and provides feedback to operators about detected foretokens and predicted abnormalities. This feedback mechanism simplifies operator decision-making by presenting processed, actionable information rather than requiring operators to manually analyze raw sensor data against predefined limits.
3Measurement precision
If manual adjustment of limit values is performed through repeated test runs, then detection accuracy can be improved, but the process requires significant labor and time investment
Solution Approach 1:
The system automatically improves its detection accuracy by continuously learning from historical plant data. It autonomously identifies patterns and correlations in the data, updating its detection models without requiring manual intervention or repeated test runs, thereby maintaining high detection accuracy while maximizing setup efficiency.
Solution Approach 2:
The system performs continuous analysis of plant data against historical patterns, maintaining constant detection capability without interruption. This continuous operation eliminates the need for periodic manual recalibration and test runs, sustaining high detection accuracy while maximizing overall system productivity.
Data Source
AI summary
A plant alarm apparatus has: plant data input means for periodically taking in plant data; a latest plant data value table for temporarily storing latest values of the plant data; plant data recording means for taking out the latest values of the data of the latest plant data value table and storing them in a plant history data table; alarm-triggering value alteration means for updating the points for detecting plant abnormality in an alarm-triggering value registration table in response to a request by an operator; alarm-triggering condition editing means for registering alarm-triggering conditions of points showing a foretoken of abnormality; deviation-from-limit-value determining means for reading the stored values of the alarm-triggering value registration table and the alarm-triggering condition table and comparing the limit values of the points for detecting abnormality; and alarm notification output means for outputting the outcome of the comparison.