Event Analysis Apparatus Using Bayesian Networks for Plant Improvement
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Solution Overview
Problem
Current event analysis systems require significant labor and time to identify improvement candidates in a plant, as they need to perceive the cause-and-effect relationship of each event to determine if improvements are necessary, which is inefficient.
Innovation Solution
An event analysis apparatus that calculates the degree of association between events using an event matrix, establishes a probabilistic cause-and-effect relationship model via Bayesian networks, and extracts improvement candidate patterns based on these relationships, allowing for automated identification of improvement candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of cause-and-effect relationships for each event is performed to determine improvement candidates, then the accuracy of improvement identification is improved, but the time and labor required increases significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated information processing system that uses event matrices and association degree calculations to identify improvement candidates, thereby reducing time and labor while maintaining accuracy
Solution Approach 2:
The system enables automated self-analysis of event data by calculating association degrees between events and automatically identifying improvement candidates without requiring manual intervention for each event analysis
2Adaptability or versatility
If comprehensive analysis of all events is performed to ensure no improvement candidate is missed, then the completeness of improvement identification is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the analysis process into distinct modules: event matrix generation, association degree calculation, and improvement candidate identification. This modular approach enables comprehensive analysis while managing system complexity through structured decomposition
Solution Approach 2:
The system uses adjustable parameters such as association degree thresholds and event types to be analyzed, allowing comprehensive coverage of improvement candidates while simplifying the analysis scope through parameter configuration rather than system structural complexity
Data Source
AI summary
An associated event group preparation unit calculates a degree of association between the events on the basis of an event matrix, and prepares an associated event group for each event. A cause-and-effect relationship model establishment unit establishes a probabilistic cause-and-effect relationship model by a Bayesian network on the basis of the event matrix, for each associated event group. An improvement candidate pattern receiving unit receives a setting of an improvement candidate pattern in which a condition of an event to be set as an improvement candidate is determined by attributes of the base point event and the associated event and a conditional probability between the base point event and the associated event. A pattern analysis unit extracts a probabilistic cause-and-effect relationship model conforming to any one of the set improvement candidate patterns, from the established probabilistic cause-and-effect relationship models for each event.