AI-Based Structural Load Prediction for Aircraft Fatigue Life
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Solution Overview
Problem
Estimating fatigue life of aircraft components is challenging due to varying structural loads from usage, making it difficult to determine reliable maintenance schedules, often leading to overly conservative replacement or retirement of components, resulting in waste and increased costs.
Innovation Solution
Implementing a system with sensors and artificial intelligence models to predict structural loads based on actual operating conditions, converting data from the frequency domain to the time domain for accurate fatigue life estimation and personalized maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional conservative methods are used to estimate fatigue life, then component reliability is ensured, but component replacement occurs prematurely leading to waste and increased costs
Solution Approach 1:
The patent transforms fatigue life estimation from using generic conservative parameters to using actual operational parameters collected from sensors. By monitoring real-world usage conditions (load, temperature, humidity, operational cycles) and feeding them into an AI model, the system dynamically adjusts fatigue life predictions to match actual component behavior, preventing premature replacement while ensuring reliability
Solution Approach 2:
The system implements continuous feedback by collecting operational data from sensors throughout the component's service life, processing this data through an AI model to update fatigue life estimates in real-time. This feedback loop allows the system to adapt predictions as actual usage patterns emerge, replacing components only when truly necessary rather than following fixed conservative schedules
2Loss of substance
If individualized fatigue life estimation is implemented using AI models, then maintenance costs are reduced, but system complexity increases
Solution Approach 1:
The patent employs a multi-functional integrated system where a single AI model performs multiple tasks: analyzing operational data, predicting fatigue life, determining optimal replacement timing, and generating maintenance recommendations. This universal approach consolidates what could be multiple separate complex systems into one cohesive platform, reducing overall system complexity while achieving individualized fatigue life estimation across different component types
Solution Approach 2:
The system enables components to essentially self-monitor and self-report their fatigue status through integrated sensors and AI analysis. Rather than requiring complex external inspection systems, the component's own operational data is automatically collected and analyzed to determine its remaining life, making the system self-sufficient and reducing the complexity of external monitoring infrastructure
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 approach allows for individualized fatigue life estimation and customized maintenance plans, reducing unnecessary replacements and costs by accurately predicting fatigue life based on actual usage data.
Implementation Method 1
convert the initial predicted load information to a time domain to generate predicted load information for the rotorcraft component by applying a transform operation to the first output data signal to generate a second output data signal in a time domain
Data Source
AI summary
In certain embodiments, a method includes accessing, by a processing device, actual operating condition information for an operating condition parameter associated with actual operation of a vehicle. The actual operating condition information corresponds to sensor measurements associated with a vehicle component of the vehicle. The method includes analyzing, by the processing device and using an artificial intelligence model, the actual operating condition information to generate predicted load information for the vehicle component. The method includes determining, according to the predicted load information, an estimated fatigue life for the vehicle component that is individualized for the vehicle component.