Aviation Component Inspection via Machine Learning
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Solution Overview
Problem
Manual aviation component inspection reports are inconsistent and time-consuming, leading to inaccuracies and difficulties in tracking inspection information across different aircraft.
Innovation Solution
An aviation component inspection device equipped with a camera, display, input device, and computer that uses machine-learning models to identify and classify components through image recognition, generating digital inspection reports with increased accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used, then flexibility and adaptability are maintained, but inspection consistency and accuracy deteriorate
Solution Approach 1:
The system enables self-service inspection by allowing the inspection device to automatically capture images, process them through machine learning models, and generate inspection reports without requiring manual analysis. The device serves itself by autonomously completing the inspection workflow while maintaining accuracy through automated image recognition and component detection algorithms.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with an automated electronic system. The mechanical action of visually examining components and manually recording findings is substituted by electronic image capture, digital processing through machine learning models, and automated report generation, thereby improving consistency and accuracy.
2Productivity
If manual inspection reporting is used, then device complexity is minimized, but productivity and inspection speed deteriorate
Solution Approach 1:
The inspection device is designed as a multi-functional universal system that combines image capture capabilities, machine learning model execution, data processing, and report generation in a single integrated platform. This universal device can inspect various aviation components across different aircraft types, thereby improving productivity without proportionally increasing complexity.
Solution Approach 2:
The system introduces an intermediary processing layer consisting of machine learning models that act as mediators between raw image data and inspection conclusions. These models automatically identify components and assess their condition, bridging the gap between simple image capture and complex inspection reporting, thereby enhancing productivity while managing system complexity through modular architecture.
3Stability of the object's composition
If manual inspection reports are used, then information consistency is maintained at human level, but data standardization and usability deteriorate
Solution Approach 1:
The system enforces homogeneity in data collection and reporting by using standardized digital formats and structured schemas for all inspection records. Every inspection follows the same digital protocol, ensuring consistent data composition across different aircraft and inspectors. This standardized approach maintains data stability while enabling efficient processing and analysis.
Data Source
AI summary
An aviation component inspection device includes a camera, a display, an input device, and a computer. The camera is configured to capture images of an aviation component under inspection. The computer is configured to receive an image from the camera, evaluate the image with one or more machine-learning aviation component-detection models. Each machine-learning aviation component-detection model is previously trained to output at least one confidence score indicating a confidence that a corresponding aviation component is present in the image. The computer is configured to present, via the display, a list of candidate aviation components based on corresponding confidence scores output by the one or more machine-learning aviation component-detection models, and add data previously-associated with a selected candidate aviation component from the list to a digital inspection report responsive to receiving user verification, via the input device, confirming the selected candidate aviation component is present in the image.