Production Line Causal Modeling for Abnormality Root Cause Estimation
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Solution Overview
Problem
Identifying the causal factors of abnormal events in production lines is complex due to the increasing number of mechanisms and changing operating conditions, making it difficult for both skilled and unskilled workers to accurately determine the cause-and-effect relationships, which existing technologies have not adequately addressed.
Innovation Solution
An information processing device and method that estimates abnormality causal factors by analyzing cause-and-effect models, using a display to visualize the results, allowing users to select models based on their input, and utilizing an event-related matter registration unit and event estimator to calculate contribution ratios and similarity degrees for simplified estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skilled workers use professional expertise to analyze cause-and-effect relationships, then accuracy of abnormality detection is improved, but complexity of operation increases and unskilled workers cannot perform the task
Solution Approach 1:
The patent introduces an information processing device as an intermediary between the complex production line systems and the maintenance workers. This device automatically analyzes cause-and-effect relationships among mechanisms using collected operation data, generating visualized cause-and-effect models that guide workers. The intermediary handles the complex analysis work, allowing unskilled workers to perform maintenance operations with the same effectiveness as skilled workers would achieve through expertise.
Solution Approach 2:
The patent replaces the mechanical system of human expertise and experience-based analysis with an automated information processing system. Instead of relying on workers' professional knowledge to interpret complex mechanism relationships, the system automatically collects data from multiple mechanisms, analyzes correlations, and presents simplified cause-and-effect models. This substitution transforms the dependency from human skill to automated computational analysis.
2Productivity
If the number of mechanisms in production line increases, then productivity is improved, but difficulty of detecting and measuring cause-and-effect relationships increases
Solution Approach 1:
The patent segments the complex analysis task into distinct functional modules: data collection from individual mechanisms, correlation analysis between mechanisms, cause-and-effect model generation, and visualization. By dividing the overall problem of analyzing multiple mechanisms into these manageable segments, the system can effectively handle increasing numbers of mechanisms without overwhelming complexity in any single analysis step.
Solution Approach 2:
The patent changes the parameters of data representation and analysis by collecting standardized operation data from multiple mechanisms and transforming it into cause-and-effect models with quantified relationship strengths. The system uses contribution rates and probability values as parameters to represent causal relationships, making complex multi-mechanism interactions measurable and analyzable even as the number of mechanisms increases.
3Measurement precision
If more abnormality data is collected to identify causal factors, then accuracy of abnormality identification is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential data elements needed for cause-and-effect analysis from the broader set of available abnormality data. Instead of processing all possible operational parameters, the system focuses on collecting and analyzing specific operation data that directly relates to mechanism interactions and abnormal events. This selective extraction maintains accuracy while reducing the complexity of data processing requirements.
Data Source
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AI summary
An information processing device (1) includes a cause-and-effect model generator (114 to 116), an abnormality detector (117), a contribution ratio calculator (118), and an estimator (119). The cause-and-effect model generator (114 to 116) 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. The abnormality detector (117) obtains a result of abnormality detection in the production line. The contribution ratio calculator (118) calculates a contribution ratio of a variable contributing to abnormality in the result of abnormality detection. The estimator (119) 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.