Aircraft Structure Diagnostics Using Neural Networks
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Solution Overview
Problem
Current methods for diagnosing damage or defects in aircraft structures are laborious and imprecise, as they do not reflect real-time physical and mechanical conditions, and require complex mathematical modeling.
Innovation Solution
A method using deformation sensors and neural networks to correlate real-time deformation data with predicted data, allowing for real-time estimation of a structure's condition without the need for a physical/mathematical model, enabling on-board diagnostics during service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical reconstruction and fatigue estimation methods are used to diagnose damage, then diagnostic coverage is provided, but the method is laborious and imprecise because it does not reflect real-time conditions
Solution Approach 1:
The patent replaces complex mathematical modeling and historical reconstruction methods with a neural network-based system that directly processes sensor data. The neural network learns the relationship between sensor readings and structural defects, substituting traditional mechanics-based fatigue estimation with a data-driven approach that provides real-time diagnostic results without requiring complex physical models.
Solution Approach 2:
The diagnostic system uses the aircraft's own operational data and sensor readings to perform self-diagnosis. The neural network is trained on historical data from the same aircraft type and then deployed to autonomously assess structural conditions during service, eliminating the need for external expert analysis and manual inspection processes.
2Measurement precision
If complex mathematical models are used for structure behavior prediction, then diagnostic accuracy is improved, but calculation complexity and implementation difficulty increase
Solution Approach 1:
The patent replaces complex mathematical models of structure behavior with a neural network that learns patterns from training data. Instead of solving differential equations or using finite element models during operation, the system uses the pre-trained neural network to directly map sensor readings to defect assessments, dramatically reducing computational complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The neural network is trained in advance using comprehensive datasets that capture various structural conditions and sensor responses. This preliminary training phase allows the network to internalize complex relationships, so that during actual operation, simple forward propagation through the network provides accurate diagnostics without requiring complex real-time calculations.
3Reliability
If traditional diagnostic methods are used, then damage detection is possible, but the method requires excessive calculation and cannot be implemented on-board during service
Solution Approach 1:
The patent replaces calculation-intensive traditional diagnostic methods with a neural network-based system that requires minimal real-time computation. The complex processing is shifted to the offline training phase, while the deployed system only needs to perform simple matrix multiplications and activation function evaluations, making it feasible for on-board implementation with standard flight computers.
Solution Approach 2:
The patent changes the operational parameters of the diagnostic system by moving from continuous complex mathematical solving to discrete neural network inference. This parameter change in the computational approach reduces the processing burden from requiring high-performance computing resources to using standard embedded systems already present in modern aircraft.
Data Source
AI summary
A method for performing diagnostics of a structure subject to loads, in particular an aircraft structure, is implemented by an arrangement of sensors located at relevant points of the structure and corresponding neural networks. The method includes training the neural network in order to establish an associative relationship between the state of the structure in a subset of relevant points and the state of the structure in at least one residual relevant point. The state of the structure is detected in a plurality of relevant points under operating conditions. The state of the structure is estimated in at least one residual relevant point by the associated neural network on the basis of the pre-established associated relationship. The state of the estimated structure is compared with the detected state at the residual relevant point, such that an intact state of the structure is determined if the expected and detected values of the state parameter match, or a defective state of the structure is determined if these values differ.