Aircraft Component OCR Identification for Distorted Part Markings
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Solution Overview
Problem
Existing methods for identifying aircraft engine components using part identifiers are time-consuming and prone to misreading due to distortion and difficulty in reading thermal-cycled identification numbers.
Innovation Solution
An aircraft component identification system utilizing a camera, support mechanism, and controller with a trained machine learning model for optical character recognition to accurately read and interpret part identifiers on engine components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual reading methods are used for part identifiers, then the system is simple to operate, but the identification process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual mechanical reading operations with an automated optical character recognition system. A camera captures images of part identifiers, and a trained machine learning model processes these images to automatically recognize and extract character sequences, eliminating the need for manual reading while significantly improving identification speed and accuracy.
Solution Approach 2:
The system enables self-service identification by automatically capturing images of part identifiers and processing them through the trained model without requiring human intervention. The camera and processing system work autonomously to identify components, reducing dependency on manual operations while maintaining simplicity in the overall process flow.
2Measurement precision
If traditional reading methods are used, then the equipment is simple, but the accuracy of reading distorted or faded identifiers is poor
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical recognition system. The camera captures high-resolution images of part identifiers, and the trained machine learning model processes these images to accurately recognize characters even when they are distorted, faded, or difficult to read, significantly improving identification accuracy.
Solution Approach 2:
The system performs preliminary image capture and processing before final identification. The camera预先 captures the identifier image, and the trained model预先 processes and analyzes the image data, preparing the information for accurate character recognition even under challenging conditions such as distortion or fading.
3Productivity
If automated recognition systems are implemented, then identification speed improves, but the system complexity and cost increase
Solution Approach 1:
The patent implements automated recognition by replacing manual reading operations with a camera-based optical character recognition system. The trained machine learning model automatically processes identifier images, enabling fast and accurate component identification that significantly improves maintenance efficiency while managing system complexity through streamlined architecture.
Solution Approach 2:
The system creates optical copies (images) of part identifiers using a camera, then processes these copies through the trained model for recognition. This approach enables automated identification without requiring direct physical interaction with the components, improving efficiency while keeping the system relatively simple by working with image data rather than physical manipulation.
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
Facilitates efficient and accurate identification of engine components, reducing human error and improving maintenance efficiency by automating the reading of distorted or faded part identifiers.
Implementation Method 1
a camera configured for capturing an image of a part identifier on an aircraft component
Implementation Method 2
perform optical character recognition on image data obtained from the image captured by the camera to identify the series of characters, including feeding the image data to a trained model, the trained model having been trained using machine learning and training data
Data Source
AI summary
An aircraft component identification system, has: a camera configured for capturing an image of a part identifier on an aircraft component of an aircraft engine, the part identifier including a series of characters; and a controller operatively connected to the camera, the controller having a processing unit and a computer-readable medium having stored thereon instructions executable by the processing unit to: perform optical character recognition on image data obtained from the image captured by the camera to identify the series of characters, including feeding the image data to a trained model, the trained model having been trained using machine learning and training data, the training data including image data sets associated with part identifier sets; and obtain information about the aircraft component using the series of characters of the part identifier.