Computer Vision Propeller Inspection System
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Solution Overview
Problem
Aerial vehicles face significant downtime due to manual or visual inspections, which are time-consuming and unable to detect operational issues between periodic checks, leading to delayed remediation and reduced operational efficiency.
Innovation Solution
The implementation of a computer vision system that captures high-resolution images of aerial vehicle components, such as propellers, using cameras and planar light sources to minimize specular reflections, and processes these images with a trained neural network to automatically detect surface flaws, allowing for real-time inspection and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or visual inspections are performed to detect surface flaws, then inspection accuracy is improved, but inspection time and operational downtime increase
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical inspection system using multiple cameras and light sources. The system captures images from multiple angles and uses image processing algorithms to automatically detect surface flaws, eliminating the need for manual inspection while maintaining high detection accuracy and reducing inspection time to minutes.
Solution Approach 2:
The patent creates detailed digital copies (images) of the propeller surface from multiple angles using high-resolution cameras. These digital replicas allow for automated analysis and flaw detection without physically touching or displacing the actual propeller, enabling rapid inspection while preserving inspection accuracy through detailed digital representation.
2Reliability
If periodic manual inspections are conducted, then structural integrity is monitored, but operational efficiency decreases due to repeated downtime
Solution Approach 1:
The patent enables continuous monitoring capability where the automated inspection system can be performed repeatedly and rapidly without requiring the propeller to be removed from service. The system maintains continuous structural integrity monitoring by enabling frequent inspections with minimal downtime, ensuring reliability while preserving operational efficiency through rapid, automated assessment.
Solution Approach 2:
The inspection system performs self-assessment of propeller integrity through automated image capture and analysis. The system independently identifies surface flaws, cracks, and defects without requiring human intervention during the inspection process, enabling rapid self-diagnosis that maintains reliability while minimizing operational disruption.
3Measurement precision
If high-resolution imaging is used to detect surface flaws, then detection precision is improved, but system complexity increases
Solution Approach 1:
The patent divides the inspection task into multiple segments by using several cameras positioned at different angles rather than relying on a single complex high-resolution camera. Each camera captures images of specific portions of the propeller surface, and the system integrates these segmented views to achieve comprehensive high-precision flaw detection while keeping individual camera components simpler and more manageable.
Solution Approach 2:
The patent introduces image processing software and algorithms as intermediaries that synthesize and analyze images from multiple camera sources. This intermediary processing layer integrates the simpler individual camera inputs into a unified high-precision inspection result, achieving superior detection precision while managing system complexity through software-based image fusion and analysis.
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
This solution enables automated and efficient inspections, reducing downtime, allowing for immediate detection of structural deficiencies and enabling timely maintenance, thereby enhancing the operational reliability and safety of aerial vehicles.
Implementation Method 1
capturing images of one or more sides of the subject and planar light sources configured to illuminate surfaces of the subject and minimize specular reflections on the surfaces
Data Source
AI summary
A convolutional neural network may be trained to inspect subjects such as carbon fiber propellers for surface flaws or other damage. The convolutional neural network may be trained using images of damaged and undamaged subjects. The damaged subjects may be damaged authentically during operation or artificially by manual or automated means. Additionally, images of undamaged subjects may be synthetically altered to depict damages, and such images may be used to train the convolutional neural network. Images of damaged and undamaged subjects may be captured for training or inspection purposes by an imaging system having cameras aligned substantially perpendicular to subjects and planar light sources aligned to project light upon the subjects in a manner that minimizes shadows and specular reflections. Once the classifier is trained, patches of an image of a subject may be provided to the classifier, which may predict whether such patches depict damage to the subject.