Borescope Video Grid Overlay for Faster Aviation Engine Defect Capture
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Solution Overview
Problem
Aviation engine inspections using borescopes are time-consuming and prone to missing defects due to inspector fatigue and inefficient video processing, leading to prolonged downtime and suboptimal defect detection.
Innovation Solution
Employing machine learning to generate a digital grid and visual layer overlay on borescope video feeds, classifying defects, and providing an augmented reality layer for real-time defect identification, enabling standardized imaging and faster inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual borescope inspection is used, then inspector expertise can be utilized, but inspection time is prolonged and defect detection accuracy decreases due to inspector fatigue
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated computer-based system that captures borescope video feeds, processes them through machine learning models, and automatically identifies defects. This substitution eliminates inspector fatigue and consistently maintains high detection accuracy while significantly reducing inspection time.
Solution Approach 2:
The system enables self-service inspection by automating the defect detection process without requiring continuous human intervention. The machine learning models independently analyze video feeds, identify defects, and generate inspection reports, allowing the inspection system to serve itself and eliminating the time-consuming manual review process.
2Measurement precision
If comprehensive video analysis is performed to ensure accurate defect detection, then defect detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing video feeds and pre-training machine learning models with extensive defect data before actual inspection. This preliminary preparation enables the system to quickly analyze videos during inspection without compromising accuracy, as the heavy computational work has already been done in advance.
Solution Approach 2:
The patent replaces manual video analysis with automated machine learning-based analysis that can process multiple video feeds simultaneously. This substitution enables comprehensive analysis of all inspection data without the time constraints of manual review, maintaining high defect detection accuracy while increasing overall inspection throughput.
3Measurement precision
If digital grid overlay is implemented for standardized imaging, then measurement precision improves, but system complexity increases
Solution Approach 1:
The patent introduces a digital grid overlay as an intermediary layer between the borescope video feed and the defect analysis process. This digital grid serves as a mediator that standardizes the imaging without requiring physical modifications to the borescope or engine, achieving measurement precision through software-based coordination rather than hardware complexity.
Data Source
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AI summary
Systems, computer-implemented methods and/or computer program products that facilitate aviation engine inspection are provided. In one embodiment, a computer-implemented method comprises: generating, by a system operatively coupled to a processor, a digital grid and visual layer overlay on a raw video feed from borescope inspections; analyzing, by the system, the video feed and identifying frames that capture information of part damage and defects; and classifying, by the system, type of part defect, determining location of defect and learning the digital grid.