Multispectral Aircraft Component Damage Detection
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Solution Overview
Problem
Current methods for detecting anomalies in aircraft components, such as visual and acoustic inspections, are prone to human error and cannot be automated, making them unreliable and susceptible to environmental factors like dirt and lighting reflections.
Innovation Solution
A multispectral image processing system using a lighting device to illuminate components with multiple spectral bands, combined with neural networks for image analysis, to generate a multispectral image and detect anomalies by estimating the probability of damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual or acoustic inspection is performed by trained personnel, then anomaly detection capability is achieved, but human error and subjectivity reduce reliability
Solution Approach 1:
The patent replaces manual visual and acoustic inspection methods with an automated optical inspection system that uses structured lighting and image processing algorithms. This substitution eliminates human subjectivity and variability, providing consistent, objective anomaly detection while maintaining high reliability through systematic image analysis.
Solution Approach 2:
The patent introduces structured lighting patterns as an intermediary between the inspection system and the component surface. These controlled light patterns interact with surface anomalies to create detectable optical signatures, enabling precise anomaly detection without direct human observation and eliminating the influence of environmental lighting conditions.
2Device complexity
If traditional visual inspection is used, then simplicity is maintained, but environmental factors like dirt and lighting reflections distort results
Solution Approach 1:
The patent uses structured lighting patterns as an intermediary that actively probes the component surface. These controlled light sources create specific optical responses that are insensitive to ambient environmental conditions, allowing the system to distinguish between actual surface anomalies and artifacts caused by dirt or reflections.
Solution Approach 2:
The patent changes the lighting parameters by using structured patterns with specific spatial frequencies and intensities. This parameter control allows the system to optimize the interaction between light and surface features, creating detectable signals from anomalies while suppressing interference from environmental factors like dirt and reflections.
3Reliability
If acoustic inspection with mechanical strikers is used, then anomaly detection is possible, but the method cannot be automated and requires skilled personnel
Solution Approach 1:
The patent replaces mechanical acoustic inspection methods with an optical-based automated system. Instead of using mechanical strikers and human interpretation of acoustic responses, the system uses structured lighting and digital image processing to automatically detect and characterize surface anomalies, achieving both high reliability and full automation.
Solution Approach 2:
The patent implements an automated inspection system that performs the complete inspection process without human intervention. The system autonomously captures images, processes the data through algorithms, identifies anomalies, and generates inspection results, making the inspection process self-sufficient and eliminating the need for skilled personnel.
4Measurement precision
If multispectral image processing with neural networks is implemented, then automation and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent segments the inspection task into distinct processing stages: image acquisition with structured lighting, preprocessing to enhance features, neural network-based anomaly detection, and result interpretation. This segmentation allows each component to be optimized independently while maintaining overall system manageability and high detection precision.
Solution Approach 2:
The patent employs a multi-functional system that combines structured lighting, multispectral imaging, and neural network processing in a single integrated platform. This universal system performs multiple functions (illumination, data acquisition, analysis, and decision-making) that would otherwise require separate devices, managing complexity while enhancing precision.
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
The system effectively detects damages in aircraft components with high accuracy, reducing human error and environmental interference, while being cost-effective and automatable.
Implementation Method 1
The lighting device (12) is controllable so as to illuminate the component (1) with a plurality of radiations having respective spectral bands
Implementation Method 2
the image acquisition device (14) acquires a corresponding image of the component (1), illuminated by the lighting beam
Data Source
Figure 1~2
Figure 3~7
Figure 4
AI summary
Method implemented by computer (16) for detecting a damage to a component (1), including: applying (212) a first neural network (40) to at least one multispectral image (25) of the component (1), formed by a plurality of images (30) of the component (1) in corresponding spectral bands, to generate a corresponding mask (62), which includes a respective plurality of pixels, each pixel of the mask (62) being relative to a corresponding portion of the component (1), the first neural network (40) being further such that each pixel of the mask (62) represents a corresponding first-level estimate, which is indicative of a probability that the portion of the component (1) to which the pixel refers is damaged; applying a second neural network (70) to a data structure (65) that is a function of the mask (62), to generate a second-level estimate (999), which is indicative of a probability that the component (1) is damaged; and detecting (228) whether the component (1) is healthy or damaged, based on the second-level estimate (999).