Aircraft Component Anomaly Inspection with Multi-Resolution Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying anomalous areas on aircraft surfaces are tedious and prone to human error, relying on heavy equipment and manual inspection, which can lead to false positives or negatives.
Innovation Solution
A computational model, such as a convolutional neural network, is trained using images of varying resolutions to identify anomalous portions of aircraft components, providing multiple determinations at different resolutions to enhance accuracy and assist human operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods using heavy equipment are used, then the inspection can be performed with current technology, but the process is tedious and prone to human error
Solution Approach 1:
The patent replaces manual mechanical inspection methods with an automated computational model that processes images to identify anomalies. The system uses machine learning algorithms to detect defective areas, substituting human operators and heavy inspection equipment with an automated digital system that reduces human error and improves reliability.
Solution Approach 2:
The patent creates digital copies of the component surfaces through imaging and uses these copies for automated analysis. The computational model processes these image copies to identify anomalies, eliminating the need for direct manual inspection of the physical component while maintaining inspection accuracy.
2Measurement precision
If high resolution images are used for inspection, then the identification accuracy of anomalies is improved, but the processing time and computational load increase
Solution Approach 1:
The patent divides the high-resolution image into multiple lower-resolution sections or patches. The computational model processes these smaller segments independently and then aggregates the results to identify anomalies in the full image. This segmentation approach maintains detection accuracy while significantly reducing computational load and processing time.
Solution Approach 2:
The patent applies the computational model to process only critical or suspicious regions of the image at high resolution, while other areas are processed at lower resolution or skipped. This partial action approach focuses computational resources on areas most likely to contain anomalies, improving efficiency without sacrificing detection accuracy.
Data Source
AI summary
A method includes training a model to identify anomalous portions of a test component using training images and labels that indicate anomalous portions of training components within the training images. The method also includes compressing a source image of the test component to generate a first input image having a first resolution and making a first determination of whether the first input image indicates that the test component is anomalous. The method also includes making a second determination, for each section of a second input image, of whether the section indicates that the test component is anomalous. The second input image has a second resolution that is greater than the first resolution. The method also includes providing an indication of whether the first input image indicates that the test component is anomalous and providing an indication of whether the second input image indicates that the test component is anomalous.