Abnormal Cause Display Using Causal Process Data Diagnosis
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Solution Overview
Problem
Current plant control monitoring devices lack accuracy in identifying the cause of abnormal irregularities in production facilities, which can impact safety, stability, quality, and cost.
Innovation Solution
An abnormal irregularity cause display device that includes a process data acquisition unit, an abnormality determination unit, a cause diagnosis unit, and an output control unit, utilizing causal relation information to calculate abnormality degrees and provide appropriate handling recommendations based on identified causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alarm monitoring methods are used to detect abnormal irregularities, then the system can monitor plant operations, but the accuracy of identifying the cause of abnormal irregularities is insufficient
Solution Approach 1:
The system pre-establishes causal relation information databases that define relationships between various causes and their influences on process data before actual abnormality detection occurs. This preliminary preparation enables rapid and accurate cause identification when irregularities are detected, without requiring complex real-time analysis of all possible causes.
Solution Approach 2:
The system introduces causal relation information as an intermediary element that mediates between raw process data and cause identification. This intermediary layer translates complex sensor data into meaningful cause diagnoses by matching observed irregularities against pre-defined causal relationships, improving accuracy without proportionally increasing system complexity.
2Reliability
If multiple sensors are used to monitor process data, then more comprehensive monitoring is achieved, but it becomes more difficult to identify the specific cause of irregularities
Solution Approach 1:
The system segments the cause diagnosis task by establishing separate causal relation information entries for different causes and their specific influences on different process data. Each cause is analyzed independently with its own set of related process parameters, allowing the system to handle multiple sensors' data without becoming overwhelmed by the complexity of analyzing all sensors simultaneously.
Solution Approach 2:
The causal relation information database serves multiple functions: it stores knowledge about cause-influence relationships, enables abnormality detection, supports cause identification, and provides a framework for analyzing data from multiple sensors. This multi-functional approach allows comprehensive monitoring while maintaining systematic cause analysis capability.
Data Source
AI summary
An abnormal irregularity cause display device includes a process data acquisition unit that reads, from a storage device, the pieces of process data, an abnormality determination unit that calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, a cause diagnosis unit that determines, for each of the pieces of process data, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity, which appears as an influence resulting from the cause, of the process data output by each of the plurality of sensors, and an output control unit that reads, from the storage device, the information indicating the handling and makes an output device output the information.