Aircraft Part Identifier OCR Under Variable Lighting
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Solution Overview
Problem
Existing methods for identifying aircraft engine components are time-consuming and prone to misreading due to distortion and difficulty in reading part identifiers, especially under varying luminosity conditions.
Innovation Solution
An aircraft component identification system using a camera and a controller with a trained machine learning model for optical character recognition, supported by a movable mechanism to align part identifiers within the camera's line of sight and adjustable lighting for clear imaging, enabling accurate identification of characters on components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reading methods are used for part identifiers, then simplicity is maintained, but reading accuracy deteriorates due to distortion and difficulty in reading
Solution Approach 1:
The patent replaces manual visual inspection and mechanical reading methods with an automated optical recognition system. A camera captures images of part identifiers, and a controller with machine learning model automatically recognizes and reads the characters, eliminating human error from distortion and difficulty in reading while maintaining operational simplicity through automation.
2Productivity
If automated optical recognition is implemented, then reading speed is improved, but system complexity increases due to additional components
Solution Approach 1:
The system employs a self-learning machine learning model that automatically improves its recognition capabilities through training data. The controller autonomously processes images, identifies part identifiers, and retrieves component information without requiring complex external systems or manual intervention, thereby achieving high reading speed while keeping the overall system architecture relatively simple.
3Measurement precision
If image capture is performed under varying luminosity conditions, then operational flexibility is maintained, but image quality deteriorates affecting character recognition
Solution Approach 1:
The system dynamically adjusts image capture parameters based on ambient luminosity conditions. The camera and lighting system modify exposure settings, gain, and illumination intensity to optimize image quality across varying light conditions. This parameter adaptation ensures high-quality images for character recognition while maintaining the ability to operate in diverse environmental conditions.
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 rapid and accurate reading of part identifiers on aircraft components, improving maintenance efficiency by providing precise information for servicing needs.
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
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AI summary
An aircraft component identification system (300), has: a camera (302) configured for capturing an image of a part identifier (33) on an aircraft component (301) of an aircraft engine (10), the part identifier (33) including a series of characters; and a controller (320) operatively connected to the camera (302), the controller (320) having a processing unit (802) and a computer-readable medium (804) having stored thereon instructions (806) executable by the processing unit (802) to: perform optical character recognition on image data obtained from the image captured by the camera (302) to identify the series of characters, including feeding the image data to a trained model (321), the trained model (321) having been trained using machine learning and training data (322), the training data (322) including image data sets associated with part identifier sets; and obtain information about the aircraft component (301) using the series of characters of the part identifier (33).