Production Line Cause-Effect Modeling for Abnormality Factor Estimation
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Solution Overview
Problem
In production lines, identifying the causal factors of abnormal events is challenging due to the complexity of relationships among numerous mechanisms, making it difficult for both skilled and unskilled workers to accurately determine the root cause of issues, especially with increasing mechanization and changing operating conditions.
Innovation Solution
An information processing device and method that generates cause-and-effect models based on relationships between mechanisms, calculates contribution ratios, and estimates abnormality causal factors, allowing for simplified identification and visualization of causal factors, even for unskilled workers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skilled workers use professional expertise and experience to identify cause-and-effect relationships among mechanisms, then the accuracy of abnormality detection is improved, but the complexity of the maintenance operation increases and requires highly skilled personnel
Solution Approach 1:
The patent introduces an information processing device as an intermediary that automatically analyzes operational data from multiple mechanisms, calculates contribution ratios using information theory, and generates visualized cause-and-effect relationship diagrams. This intermediary system bridges the gap between complex multi-mechanism interactions and user-friendly abnormality identification, eliminating the need for workers to manually trace complex causal relationships while maintaining high detection accuracy
Solution Approach 2:
The patent replaces the mechanical system of human expert analysis with an automated information processing system. Instead of relying on skilled workers' professional expertise and experience to manually identify cause-and-effect relationships, the system automatically processes operational data, calculates contribution ratios, and generates visualized diagnostic information, thereby improving ease of operation while maintaining measurement precision
2Productivity
If the number of mechanisms in the production line increases, then the productivity of the production line is improved, but the difficulty of grasping cause-and-effect relationships among mechanisms increases
Solution Approach 1:
The patent extracts the essential diagnostic information from complex multi-mechanism operational data by calculating contribution ratios that quantify each mechanism's influence on abnormal events. The system separates the complex web of inter-mechanism relationships into ranked causal factors, presenting only the most significant relationships to users. This extraction approach allows the system to handle increasing numbers of mechanisms without proportionally increasing the complexity of analysis required
Solution Approach 2:
The patent transforms raw operational data from multiple mechanisms into meaningful diagnostic parameters through information theory-based contribution ratio calculations. By changing the parameter representation from raw multi-dimensional mechanism data to ranked contribution ratios, the system enables effective analysis of cause-and-effect relationships even as the number of mechanisms increases, thereby supporting higher productivity without overwhelming complexity
3Measurement precision
If more abnormality data is collected to identify exact causes of abnormal events, then the accuracy of causal factor identification is improved, but the complexity of data analysis and processing increases
Solution Approach 1:
The information processing device serves as an intermediary that automatically handles the complexity of analyzing large volumes of abnormality data. The system collects operational data from multiple mechanisms, applies information theory-based calculations to determine contribution ratios, and presents simplified diagnostic results. This intermediary approach maintains high accuracy in causal factor identification while eliminating the need for users to manually analyze complex datasets
Solution Approach 2:
The patent creates a simplified copy or representation of the complex abnormality data through visualized cause-and-effect relationship diagrams. Instead of requiring users to directly analyze raw operational data from multiple mechanisms, the system generates an accessible visual representation that preserves the essential causal relationships while reducing analytical complexity. This copying approach enables accurate causal factor identification without exposing users to the full complexity of the underlying data
Data Source
AI summary
An information processing device including a CPU and a memory storing a program. The CPU generates a plurality of cause-and-effect models between variables based on a relationship between a plurality of mechanisms in a production process of a production line, a dependency between a plurality of events associated with the plurality of mechanisms, or a control-based relationship between the plurality of mechanisms, obtains a result of abnormality detection in the production line, calculates a contribution ratio of a variable contributing to abnormality in the result of abnormality detection, and estimates an abnormality causal factor for at least one of the plurality of cause-and-effect models based on the calculated contribution ratio of the variable contributing to abnormality.