Automated Borescope Defect Tracking for Engine Blades
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Solution Overview
Problem
The manual inspection of engine blades in jet engines is time-consuming and lacks standardization, making it difficult to reliably detect defects such as notches or indentations.
Innovation Solution
A method using a video borescope that automatically detects defects by tracking the movement of engine blades through image recognition, comparing successive frames to identify defects and confirming their presence over a predefined length.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to assess engine blades, then the inspector can visually identify defects, but the inspection process is time-consuming and lacks standardization
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated optical measurement system. A camera captures images of engine blades during rotation, and image processing algorithms automatically detect defects, substituting the inspector's visual assessment with machine-based optical detection and computational analysis.
Solution Approach 2:
The system enables self-service inspection by automatically capturing blade images during normal rotation and performing defect detection without requiring manual intervention. The automated image processing and defect identification algorithms allow the system to inspect itself, eliminating the need for external inspectors.
2Ease of operation
If manual inspection is used to assess engine blades, then visual assessment can be performed, but the outcome does not satisfy standardized appraisal requirements
Solution Approach 1:
The patent transforms the inspection process from subjective visual parameters to objective quantitative parameters. Image processing algorithms analyze pixel data, edge detection results, and geometric features to precisely measure blade dimensions and detect defects, replacing subjective visual assessment with quantifiable measurements.
Solution Approach 2:
The system introduces an intermediary image processing layer between the camera and defect identification. This intermediary layer processes raw images through edge detection, feature extraction, and pattern recognition algorithms, enabling precise and standardized defect detection while maintaining operational simplicity.
3Productivity
If automated image recognition is used to identify defects, then defect detection speed increases, but false positives and negatives may occur
Solution Approach 1:
The patent segments the defect detection process into multiple independent stages: image capture, edge detection, feature extraction, pattern recognition, and verification. Each stage processes specific aspects of the blade imagery, allowing parallel processing that increases speed while maintaining accuracy through distributed analysis.
Solution Approach 2:
The system implements feedback mechanisms where detection results are verified against multiple criteria and cross-checked with historical data. The image processing algorithms continuously refine their detection accuracy by comparing new findings with established patterns, reducing false positives and negatives while maintaining high inspection speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method significantly reduces the time required for inspection, achieves a defect recognition accuracy comparable to manual inspection, and minimizes false positives and negatives, ensuring high reliability and standardization.
Implementation Method 1
a video borescope (2) is inserted into the engine (80) in such a way that
Implementation Method 2
The possible defect on the video image along the detected movement is tracked by optical image recognition
Data Source
AI summary
A method automatically detects defects during borescoping of an engine. A video borescope is inserted into the engine such that, engine blades can be moved successively through the image region of the borescope. A possible defect on an engine blade is identified by image recognition on the basis of a video borescope frame. The movement of the engine blades in the image region is detected by comparing successive frames. The possible defect is tracked by optical image recognition on the basis of the successive frames used for detecting the movement. In a condition where a trace of the possible defect on the video image corresponds to the detected movement in terms of direction and speed over a predefined length, the possible defect is identified as an actual defect.