Inspection Support System for Non-Destructive Testing
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Solution Overview
Problem
Current non-destructive inspection methods, such as radiographic testing of welded pipes, rely heavily on individual operator skills, leading to inefficiencies and variations in results due to the dependence on the operator's expertise and workload, making the inspection process labor-intensive and prone to skill-based inconsistencies.
Innovation Solution
An inspection support system comprising multiple determination devices and a learning device that collaboratively determine pass or fail in non-destructive inspections, where the learning device learns and refines the determination algorithm using aggregated inspection data from multiple sources, reducing operator reliance and improving precision through automated and iterative learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If operator skill-based learning is used in neural network training, then the system can adapt to specific inspection scenarios, but the efficiency and consistency deteriorate due to dependence on individual operator expertise
Solution Approach 1:
The patent merges multiple determination devices and their respective determination results into a single learning dataset. By combining data from multiple operators and devices, the system creates a comprehensive learning resource that captures diverse inspection scenarios without relying on any single operator's expertise, thus improving both adaptability and learning efficiency
Solution Approach 2:
The system implements feedback by using determination results from multiple determination devices as training data for the learning device. The learning device continuously improves the determination algorithm by processing feedback from real-world inspection results, enabling automated learning that does not depend on individual operator skills while maintaining high adaptability
2Adaptability or versatility
If manual inspection by operators is used, then flexibility in handling diverse cases is maintained, but the workload and time consumption increase significantly
Solution Approach 1:
The determination device performs automatic determination using the determination algorithm, enabling the system to handle diverse inspection cases independently without requiring constant operator intervention. The learning device continuously improves the algorithm through self-learning from accumulated determination results, reducing both workload and inspection time while maintaining flexibility
Solution Approach 2:
The system performs preliminary automatic determination using the determination algorithm before operator review. This preliminary action filters out clear cases that do not require human intervention, significantly reducing inspection time while maintaining the ability to handle diverse cases through operator review when needed
3Adaptability or versatility
If individual operator skills are relied upon, then customization to specific inspection needs is possible, but consistency and reliability deteriorate due to human variability
Solution Approach 1:
The determination algorithm serves as a universal solution that can be applied across multiple determination devices and inspection scenarios. The learning device aggregates determination results from multiple sources to create a comprehensive dataset that captures various inspection needs, enabling the single algorithm to handle diverse cases consistently without relying on individual operator skills
Solution Approach 2:
The system uses feedback from multiple determination results to continuously improve the determination algorithm's reliability. By processing determination results from multiple operators and devices, the learning device identifies patterns and improves consistency across different inspection cases, reducing human variability while maintaining customization capability
Data Source
AI summary
An inspection support system comprising: determination devices that determine pass or fail based on a result of non-destructive inspection of the object; and a learning device that learns a determination algorithm used to determine pass or fail based on information collected from the determination devices. The determination device transmits an ultimate determination result yielded by an inspection person who has checked a determination result to the learning device along with the corresponding result of non-destructive inspection of the object. The learning device includes: a determination result reception unit that receives the ultimate determination result and the result of non-destructive inspection of the inspection object; a learning unit that learns the determination algorithm based on received information; and a provision unit that provides the learned determination algorithm to the determination devices.