Aircraft Structural Load Estimation via Neural Network Impact Analysis
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Solution Overview
Problem
Current systems for detecting hard landings in aircraft are inefficient due to the need for additional sensors, high costs, complex integration, and inability to accurately measure stresses at specific locations, leading to unnecessary maintenance and inspections.
Innovation Solution
A method using neural networks to estimate load criteria on structural components by determining the instant of impact and calculating load criteria from measured parameters, allowing for precise stress estimation without additional sensors, and providing threshold comparisons for inspection decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specific sensors are incorporated to measure loads at various locations, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a neural network to create a virtual model (copy) of the structural component's stress state based on data from conventional sensors. Instead of physically measuring stress at multiple locations with specialized sensors, the system copies the behavior of stress sensors through software modeling, eliminating the need for complex physical sensor integration while maintaining measurement capability
Solution Approach 2:
The patent replaces the mechanical sensor system with a computational model. The neural network substitutes physical stress sensors by processing data from conventional acceleration and load sensors to calculate stress values, thereby eliminating the need for complex mechanical sensor integration in inaccessible regions
2Device complexity
If conventional sensors are used alone, then device complexity is reduced, but measurement precision of structural stresses deteriorates
Solution Approach 1:
The patent introduces a neural network as an intermediary between conventional sensors and stress measurement. The neural network acts as a mediator that transforms data from simple acceleration and load sensors into accurate stress estimates, enabling precise structural stress measurement without requiring complex sensor systems
Solution Approach 2:
The patent transforms the type of parameters being measured by using the neural network to convert raw sensor data (acceleration, load) into derived parameters (stress values at specific locations). This parameter transformation allows conventional sensors to provide precise stress measurement information through computational processing
3Productivity
If peak values of measured parameters are combined, then productivity is improved, but measurement precision deteriorates due to timing mismatches
Solution Approach 1:
The patent applies preliminary action by training the neural network offline before actual use. During training, the system learns the correct temporal relationships between different sensor parameters and stress responses. This pre-learning ensures that during real-time operation, the network can accurately combine parameters without timing mismatches, maintaining both speed and precision
Data Source
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AI summary
The present invention relates to methods and systems for estimating a loading criterion relating to the load (C) experienced by a structural component (100, 102, 104) of an aircraft (10), and assistance with detecting a so-called “hard” landing. These methods involve measuring (50) parameters of said aircraft and calculating (54) at least one loading criterion for the loading of said structural component using at least one neural network (36) receiving said parameters as input. Assistance with detecting a hard landing then requires the determining (72, 76) of a time (t0, t-i) of impact (14, 16) of said aircraft on a landing strip (12) from said measured parameters, then estimating (74, 78) a plurality of said parameters at said determined time of impact so as to calculate (54) the at least one loading criterion relating to the loading of the structural component.