Ensemble Plant Abnormality Detection for Early Alarm Accuracy
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Solution Overview
Problem
Conventional plant monitoring systems generate alarms only when significant damage occurs, leading to inefficient operation stoppages and maintenance, as they lack early warning capabilities for approaching risk states in equipment operation parameters.
Innovation Solution
A plant abnormality detection system that collects real-time data, uses ensemble learning with parametric and non-parametric models to predict normal states, and generates alarms by comparing actual data with predicted values, reducing erroneous alarms and enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional monitoring devices generate alarms only when significant damage occurs, then the alarm system is simple and easy to operate, but operation efficiency is reduced due to unnecessary stoppages and maintenance
Solution Approach 1:
The system performs preliminary actions by learning normal operation patterns from historical data before actual abnormalities occur. The modeling unit creates prediction models during normal operation, enabling early detection when deviations occur, thus avoiding unnecessary stoppages while maintaining high detection accuracy
Solution Approach 2:
The system applies partial action by selectively monitoring only when prediction deviations exceed thresholds. Instead of continuous full-scale monitoring, it activates detailed analysis only when necessary, improving efficiency while maintaining reliability through targeted detection
2Measurement precision
If a single prediction model is used, then the system complexity is low, but prediction accuracy is insufficient for early abnormality detection
Solution Approach 1:
The system merges multiple prediction models (parametric and non-parametric) into a unified ensemble framework. The modeling unit combines ARX, NARX, and neural network models, allowing each to contribute its strengths while achieving superior prediction accuracy through collective decision-making
Solution Approach 2:
The system segments the prediction task into multiple specialized models handling different aspects of plant behavior. Parametric models capture linear relationships while non-parametric models handle nonlinear patterns, dividing the complex prediction problem into manageable segments that can be optimized independently
3Measurement precision
If real-time data collection and multiple model learning are implemented, then early abnormality detection accuracy is improved, but computational resources and system complexity increase
Solution Approach 1:
The system performs partial learning by updating models selectively based on data quality and operational conditions. Instead of continuously retraining all models with all data, it processes only relevant data segments when conditions warrant, reducing computational overhead while maintaining detection accuracy
Solution Approach 2:
The system implements self-service through automated model selection and data filtering mechanisms. The learning unit autonomously determines which data to process and which models to update without external intervention, optimizing resource usage while maintaining high detection performance
Data Source
AI summary
The present disclosure provides a plant abnormality detection system and method, which can learn the plant data collected in real time through a plurality of prediction models having different characteristics to generate a prediction value having the highest accuracy to diagnose the abnormality thereof, thus detecting accurately the abnormality of the plant to early provide alarm.The plant abnormality detection system disclosed includes a data collection unit for collecting the plant data, a learning model selection unit for selecting a plurality of models in order to predict a value of the plant data, and an abnormality alarm unit including a prediction algorithm unit having a plurality of prediction algorithms, an ensemble learning unit for outputting a final prediction data by performing ensemble learning based on the prediction data outputted from the prediction algorithm unit, and an alarm logic for determining whether or not the plant is abnormal by comparing the data collected in the data collecting unit with the final prediction data.