Production Line Abnormality Causal Factor Estimation Using Surrogate Models
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Solution Overview
Problem
Existing methods lack an efficient and simplified way to identify abnormality causal factors in production line mechanisms, making it difficult for both skilled and unskilled workers to accurately determine the cause of abnormal events, especially with increasing complexity and dynamic operating conditions.
Innovation Solution
An information processing device and method that utilizes a surrogate model calculator based on logistic regression to estimate abnormality causal factors, combined with a cause-and-effect model generator to visualize relationships between mechanisms, allowing for the transmission of screen data for easy operator interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods based on professional expertise are used to identify cause-and-effect relationships, then measurement precision may be maintained for skilled workers, but ease of operation deteriorates for unskilled workers and device complexity increases
Solution Approach 1:
The patent introduces an information processing device as an intermediary that automatically analyzes status data from multiple mechanisms, detects abnormalities, calculates surrogate models, and estimates causal factors. This mediator translates complex inter-mechanism relationships into automated analysis, eliminating the need for human experts to manually trace cause-and-effect relationships while providing clear visual output that any operator can understand.
Solution Approach 2:
The patent replaces the mechanical system of human expert judgment and manual analysis with an automated information processing system. The device uses abnormality detection units, surrogate model calculation units, and estimator units to automatically process status data and identify causal factors, substituting human cognitive processes with computational algorithms that can handle complex multi-mechanism relationships objectively and consistently.
2Productivity
If the number of mechanisms in production line increases, then productivity is improved, but device complexity and difficulty of detecting and measuring cause-and-effect relationships increase
Solution Approach 1:
The information processing device is designed with multi-functional units that can handle various types of status data from different mechanisms uniformly. The abnormality detection unit, surrogate model calculation unit, and estimator unit form a universal processing framework that can analyze relationships among any number of mechanisms without requiring separate analysis methods for each case, enabling the system to scale with production line complexity.
Solution Approach 2:
The patent introduces an information processing device as an intermediary that automatically analyzes status data from multiple mechanisms, detects abnormalities, calculates surrogate models, and estimates causal factors. This mediator translates complex inter-mechanism relationships into automated analysis, eliminating the need for human experts to manually trace cause-and-effect relationships while providing clear visual output that any operator can understand.
3Reliability
If routine checks are performed regularly to detect abnormal events, then reliability is improved, but loss of time increases due to shutdowns and manual inspection
Solution Approach 1:
The information processing device enables continuous monitoring and analysis of status data from all mechanisms without interrupting production. The automated abnormality detection and causal factor estimation operate continuously, identifying issues in real-time or near-real-time, which allows maintenance to be scheduled during planned停机 periods rather than causing unexpected shutdowns, thus maintaining continuous useful action in the production line.
Solution Approach 2:
The surrogate model calculation and causal factor estimation provide preliminary identification of abnormality causes before actual failures occur. By continuously analyzing status data and estimating causal factors, the system detects predictive signs of abnormality in advance, allowing maintenance workers to perform routine checks proactively and prevent actual failures, thereby maintaining continuous operation.
Data Source
AI summary
An information processing device is provided that includes an obtaining unit configured to obtain status data of a plurality of mechanisms in a processing step carried out in a production line, an abnormality detector configured to detect an abnormality based on the status data obtained by the obtaining unit, a surrogate model calculator configured to calculate a surrogate model based on the status data at time of detecting the abnormality, and an estimator configured to estimate an abnormality causal factor based on the surrogate model calculated earlier.