Avionics Machine Learning for Aircraft State Prediction
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Solution Overview
Problem
Current aircraft performance modeling techniques are limited by their reliance on generic, static models that fail to accurately predict the behavior of individual aircraft, especially when faced with variability in flight conditions, leading to suboptimal optimizations and lack of convergence in large-scale processes.
Innovation Solution
The use of machine learning algorithms, including unsupervised and supervised learning methods, to predict aircraft state by analyzing flight data from sensors and avionics, allowing for the development of personalized, dynamic models that learn and adapt in real-time, independent of manufacturer-provided models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic static models (BADA, EUCASS) are used for aircraft performance modeling, then the modeling process is simple and standardized, but the prediction accuracy deteriorates due to inability to capture individual aircraft variability and complex flight conditions
Solution Approach 1:
The patent transitions from static generic models to dynamic machine learning models that adapt to individual aircraft characteristics and varying flight conditions. The ML model continuously learns from flight data to capture temporal variations and nonlinear relationships, resolving the contradiction by making the model dynamic rather than static.
Solution Approach 2:
The patent changes the fundamental parameters of the modeling approach by replacing traditional physics-based model parameters with data-driven parameters learned from flight recordings. This allows the system to capture complex variations in aircraft behavior without requiring detailed physical models of every component.
2Reliability
If machine learning models are used to capture individual aircraft variability, then prediction accuracy improves, but the model complexity and data processing requirements increase
Solution Approach 1:
The patent implements self-service through automated machine learning pipelines that automatically train, validate, and deploy models without extensive manual intervention. The system autonomously processes flight data, identifies patterns, and generates predictions, reducing the operational complexity despite increased model sophistication.
Solution Approach 2:
The patent substitutes traditional mechanical modeling approaches (physics-based equations) with computational machine learning systems. This replacement allows capturing complex aircraft variability through data-driven patterns rather than detailed mechanical models, improving robustness while managing complexity through computational methods.
3Productivity
If end-to-end machine learning is applied to predict aircraft state, then the optimization convergence improves, but the computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive flight data before deployment. This offline training phase captures complex patterns and relationships, allowing the model to make rapid predictions during actual flight operations without requiring extensive real-time computation, thus improving convergence while managing processing time.
Solution Approach 2:
The patent uses dynamic model adaptation where the ML model can be continuously refined and updated based on new flight data. This allows the system to improve optimization convergence over time while maintaining efficient real-time prediction capabilities through the learned patterns stored in the model structure.
Data Source
AI summary
Systems and methods for managing the flight of an aircraft, include the steps of receiving data from recordings of the flight of an aircraft; the data comprising data from sensors and/or data from the onboard avionics; determining the aircraft state at a point N on the basis of the received data; determining the state of the aircraft at the point N+1 on the basis of the state of the aircraft at point N by applying a model learnt by means of machine learning. Developments describe the use of the flight parameters SEP, FF and N1; offline and/or online unsupervised machine learning, according to a variety of algorithms and neural networks. Software aspects are described.