Aircraft State Prediction Using Learned Flight Data Models
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Solution Overview
Problem
Current methods for predicting the future state of an aircraft are limited by their reliance on generic, static models that fail to account for individual aircraft variability and behavioral dynamics, leading to suboptimal performance optimizations, particularly in converging towards local minima and being non-robust to real-world behavioral variations.
Innovation Solution
The implementation of machine learning techniques, including unsupervised and supervised learning methods, to determine the future state of an aircraft by applying models learned from flight data, using parameters such as SEP, FF, and N1, allowing for end-to-end performance calculation and trajectory prediction, independent of manufacturer-provided models, and enabling continuous learning from flight data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based approaches incorporating aircraft physics equations are used, then the prediction is based on theoretical models, but the method is not robust to behavior variability of individual aircraft compared to average modeled aircraft
Solution Approach 1:
The system continuously collects flight data from multiple aircraft and uses machine learning models to learn actual behavior patterns. This feedback loop allows the system to adapt to individual aircraft characteristics while maintaining robustness through aggregation of data from the fleet, resolving the contradiction between theoretical model reliability and individual variability adaptation.
Solution Approach 2:
The machine learning models automatically learn and adapt to each aircraft's specific behavior patterns without requiring manual modeling or adjustment. The system self-adjusts to individual aircraft characteristics by processing actual flight data, eliminating the need for aircraft-specific theoretical model calibration while maintaining robustness through data-driven learning.
2Productivity
If generic static models are used for predicting aircraft future state, then the implementation is simple, but the optimization performance is suboptimal and converges to local minima
Solution Approach 1:
The system transitions from static generic models to dynamic machine learning models that continuously adapt to changing flight conditions and individual aircraft characteristics. This dynamic approach enables the system to escape local minima by learning from actual flight data and adjusting predictions in real-time, improving optimization performance while managing complexity through efficient model architectures.
Solution Approach 2:
The machine learning models learn optimal parameters from actual flight data rather than relying on fixed theoretical parameters. By continuously adjusting model parameters based on observed aircraft behavior, the system achieves superior optimization performance while the modular architecture manages computational complexity through parameter efficiency.
3Measurement precision
If end-to-end machine learning is applied for performance calculation and trajectory prediction, then accurate real-time prediction is achieved, but computational requirements increase
Solution Approach 1:
The system performs preliminary training of machine learning models offline using historical flight data, creating pre-trained models that can be deployed for real-time predictions. This preliminary action separates the computationally intensive training phase from the operational phase, achieving high prediction accuracy in real-time with reduced computational power requirements during actual flight operations.
Data Source
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AI summary
The document concerns systems and methods for managing the flight of an aircraft, including the steps of receiving data (200) from aircraft flight recordings; said data including data from sensors and/or data from the onboard avionics; determining the aircraft state at point N (220) from the received data (200); determining the aircraft state at point N+1 (240) from the aircraft state at point N (220), by applying a model learned by machine learning (292). Developments describe the use of flight parameters SEP, FF, and NI; unsupervised, offline, and/or online machine learning, according to a variety of algorithms and neural networks. Software aspects are described.