Adaptive Borescope Inspection With Real-Time Parameter Adjustment
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Solution Overview
Problem
Existing video inspection systems for industrial machines are inefficient and error-prone, often requiring repetitive inspections due to inaccurate or insufficient data analysis, which can lead to delayed detection of defects threatening machine integrity.
Innovation Solution
An adaptive inspection system that analyzes inspection data in real-time, adjusts inspection parameters based on image recognition and user input, and guides operators through additional inspections or data capture methods to ensure high-fidelity image acquisition and video recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video inspection systems are used, then inspection coverage can be achieved, but inspection efficiency is low and repetitive inspections are required due to inaccurate data analysis
Solution Approach 1:
The system implements real-time feedback by analyzing inspection images through machine learning models and automatically adjusting inspection parameters. The analytical model processes images, identifies defects, and provides feedback signals that modify subsequent inspection operations, creating a closed-loop system that improves both efficiency and accuracy through continuous learning and adaptation.
Solution Approach 2:
The system dynamically changes inspection parameters such as lighting conditions, camera focus, and imaging depth based on real-time analysis. When the analytical model detects specific defect characteristics or image quality issues, it automatically adjusts these parameters to optimize defect detection, thereby reducing repetitive inspections while maintaining high accuracy.
2Loss of time
If manual inspection methods are used, then flexibility in inspection can be maintained, but time consumption increases due to repetitive tasks and delayed defect detection
Solution Approach 1:
The inspection system performs self-service through automated image analysis and defect detection. The machine learning model independently processes inspection images, identifies defects, and generates adjustment signals without requiring continuous manual intervention. This automation reduces inspection time while the system maintains adaptability through real-time parameter adjustments based on its own analysis.
Solution Approach 2:
The system replaces manual inspection mechanisms with automated digital imaging and analytical models. Instead of relying on human operators to visually inspect and manually note defects, the system uses cameras, lighting devices, and machine learning algorithms to automatically detect and analyze defects, significantly reducing time consumption while improving detection consistency.
3Measurement precision
If inspection parameters are fixed, then system operation is simple, but image quality and defect detection capability are insufficient
Solution Approach 1:
The system transitions from fixed to dynamic inspection parameters, where lighting intensity, camera focus, and imaging depth automatically adjust based on real-time image analysis. The analytical model continuously evaluates image quality and defect characteristics, dynamically modifying parameters to optimize measurement precision while maintaining manageable system complexity through automated control.
Data Source
AI summary
A method of adaptive inspection includes receiving data characterizing one or more images of an inspection region of an industrial machine acquired by an inspection system operating based on a first set of operating parameters. The inspection region includes a site feature. The method also includes determining, by an analytical model, one or more characteristics of the inspection region from the received data characterizing the one or more images of the inspection region. The method further includes generating a control signal based on the one or more characteristics of the inspection region and/or a user input. The inspection system is configured to perform a new inspection of the inspection region based on the control signal.