Facility Sensor Group Analysis for Accurate Abnormality Detection
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Solution Overview
Problem
In large-scale plant facilities, determining abnormalities is challenging due to the complexity of sensor data from numerous sensors, as detection signals often do not exceed thresholds, and skilled operators are limited, leading to inaccurate abnormality detection and prediction of future issues.
Innovation Solution
An examining apparatus that acquires sensor data from targeted sensors, learns analysis models using machine learning, and applies these models to detect abnormalities, incorporating user feedback to improve accuracy and reduce reliance on operator expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are installed in facilities to monitor abnormalities, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the large set of sensor data by dividing sensors into multiple groups based on their spatial or functional relationships. Each group is processed independently through separate analysis models, transforming the complex task of analyzing all sensors simultaneously into multiple manageable sub-tasks. This segmentation reduces the computational burden and system complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent introduces analysis models as intermediary components between the sensors and the abnormality determination system. These models process sensor data and generate intermediate results that are easier to interpret and combine. The intermediary models transform raw sensor readings into meaningful indicators, simplifying the overall system architecture and improving detection accuracy.
2Ease of operation
If detection thresholds are set for individual sensors to identify abnormalities, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent merges the detection logic from multiple sensors and multiple analysis models into a unified abnormality determination. Instead of relying on simple threshold comparisons of individual sensors, the system combines results from multiple group analyses, capturing complex patterns and correlations that single-sensor thresholds cannot detect. This merging improves precision while maintaining operational simplicity through automated integration.
Solution Approach 2:
The patent transforms the detection approach by changing from fixed threshold parameters to dynamic analysis model parameters. The analysis models learn optimal detection criteria from data, adapting to complex patterns and relationships among sensors. This parameter transformation enables more accurate detection while keeping the system easy to operate, as the models automatically adjust to changing conditions without manual threshold tuning.
3Measurement precision
If skilled operators manually monitor sensor correlations to determine abnormalities, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform the complex task of monitoring sensor correlations and determining abnormalities. The analysis models autonomously process sensor data, identify patterns, and generate abnormality determinations without requiring skilled operator intervention. This automation maintains high detection precision while dramatically improving productivity by eliminating manual monitoring bottlenecks.
Solution Approach 2:
The patent replaces the mechanical system of manual operator monitoring with an automated computational system. Instead of operators visually inspecting and analyzing sensor correlations, the system uses analysis models to automatically process data and detect abnormalities. This substitution preserves the high measurement precision achieved by expert operators while eliminating the productivity limitations of manual analysis.
Data Source
AI summary
To easily perform examination of at least one facility based on detection signals of a plurality of sensors installed in the facility. Provided are an examining apparatus, an examining method and a recording medium, including: a group designation acquiring unit to acquire designation of a targeted group including a plurality of targeted sensors to be analyzed among a plurality of sensors installed in at least one facility; a sensor data acquiring unit to acquire sensor data from each targeted sensor included in the targeted group; a learning unit to learn an analysis model by using the sensor data from each targeted sensor included in the targeted group; and an examining unit to examine the facility by using the learned analysis model.