Aircraft Component Distress Classification via Multi-Layer Network
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Solution Overview
Problem
Manual inspection of aircraft components is time-consuming and prone to inaccuracies due to human error, making it inefficient for detecting distress such as wear and tear, defects, or anomalies in components like gas turbine engine blades.
Innovation Solution
A method and system utilizing a multi-layer network image classification model, including a multi-scale fully convolutional model, to automatically classify distress in aircraft components by processing digital images, generating segmentation maps, and determining distress levels based on pixel values, thereby facilitating reliable and efficient inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to assess aircraft component distress, then human judgment and flexibility are applied, but the process is time-consuming and prone to inaccuracies
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system that captures images of aircraft components and uses computer algorithms to detect distress indicators. This substitution eliminates human error and time constraints while maintaining high detection accuracy through systematic image analysis of cracks, corrosion, and other defects.
Solution Approach 2:
The system creates digital copies (images) of the physical aircraft components and performs inspection on these copies rather than requiring direct manual examination. This allows multiple analyses to be performed simultaneously on the same component image, reducing inspection time while preserving detailed visual information for accurate distress assessment.
2Productivity
If automated image processing is implemented for distress classification, then inspection speed and consistency are improved, but system complexity increases
Solution Approach 1:
The inspection system is divided into distinct functional modules: image capture, image processing, distress detection, and classification. Each module performs a specific task, making the overall complex system manageable through modular design. This segmentation allows for independent optimization of each component while maintaining high overall productivity.
Solution Approach 2:
The system is designed to autonomously perform the complete inspection process from image capture to distress classification without requiring constant human intervention. The automated algorithms independently analyze images, detect defects, and generate assessments, reducing operational complexity despite the sophisticated technology involved.
3Reliability
If multi-layer network models are used for distress classification, then classification accuracy and reliability are enhanced, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary image processing and preprocessing steps before applying the computationally intensive multi-layer network model. By preparing images in advance (resizing, normalization, feature extraction), the system reduces the computational burden during the actual classification phase, maintaining high reliability while managing energy consumption more efficiently.
Data Source
AI summary
Systems and methods for automatically identifying and classifying distress of an aircraft component are provided. In one embodiment, a method includes accessing one or more digital images captured of the aircraft component and providing the one or more digital images as an input to a multi-layer network image classification model. The method further includes generating a classification output for the one or more images from the multi-layer network image classification model and automatically classifying the distress of the aircraft component based at least in part on the classification output.