Aircraft Wing Motion Prediction Using Sensor Fusion
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Solution Overview
Problem
Existing technologies for detecting and predicting aircraft wing motion are limited to reactive systems that only provide insights after flight events have occurred, failing to offer a proactive approach to analyzing forces and movements during flight, which can lead to wear, damage, and chaotic aircraft behavior.
Innovation Solution
The implementation of machine learning techniques, specifically using neural networks, in conjunction with data from non-contact sensors like LIDAR and cameras, or contact sensors such as accelerometers, to track and predict wing motion in real-time, enabling early detection of aerodynamic events and facilitating proactive responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive measurement systems are used to detect wing motion, then structural issues can be understood after they occur, but proactive prevention and early detection are not achieved
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict wing motion and structural issues before they occur. The system analyzes historical and real-time sensor data to forecast future wing behavior, enabling preventive measures to be taken before actual structural problems develop, thus transitioning from reactive to proactive monitoring.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from accelerometers, LIDAR, and cameras is constantly fed into machine learning models that predict future wing motion. This predicted information feeds back to the control system, which can adjust flight parameters in real-time to prevent problematic wing behavior, creating a closed-loop proactive safety system.
2Measurement precision
If multiple sensor types (LIDAR, cameras, accelerometers) are integrated with machine learning, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent merges multiple sensor types (LIDAR, cameras, accelerometers) into a unified data processing architecture that feeds into machine learning models. By combining these diverse data sources and processing them through integrated neural networks, the system achieves high prediction accuracy while managing complexity through unified processing pipelines rather than separate independent systems.
Solution Approach 2:
The machine learning platform serves multiple functions: it processes data from various sensor types, predicts wing motion, identifies structural issues, and provides control recommendations. This multi-functional approach reduces overall system complexity by using a single versatile AI core rather than multiple specialized subsystems.
3Speed
If real-time data processing is implemented to enable quick reactions, then response speed exceeds human capabilities, but computational resources and processing power are consumed
Solution Approach 1:
The system applies partial action by processing only the most critical and relevant features from sensor data in real-time, rather than analyzing every possible parameter. The machine learning models are trained to identify and focus on key indicators of wing motion and structural issues, enabling fast response with reduced computational overhead.
Solution Approach 2:
Computational energy is optimized by performing preliminary data processing, filtering, and feature extraction before main analysis. Historical data is pre-processed and stored in optimized formats, allowing the real-time system to work with pre-computed features rather than raw data, significantly reducing on-flight computational energy requirements.
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 solution allows for the prediction of potential damage and chaotic wing behavior, improving safety and efficiency during flight by enabling quicker reactions to aerodynamic events than human capabilities, and providing data for enhancing future flight operations.
Implementation Method 1
non-contact sensors (e.g., light detection and ranging (LIDAR))
Implementation Method 2
light detection and ranging (LIDAR)
Implementation Method 3
cameras
Implementation Method 4
contact sensors (e.g., accelerometers)
Data Source
AI summary
Systems, devices, and methods for tracking and/or predicting motion of a wing of an aircraft are disclosed herein. The systems, devices, and methods track wing motion (e.g., in real-time). In some embodiments, the systems and devices include stereo binocular vision (SBV) cameras and/or light detection and ranging (LIDAR) emitters and receivers mounted on the aircraft. In these and other embodiments, the systems and devices include a network of contact sensors (e.g., accelerometers or strain gauges) mounted on a wing and corresponding receivers mounted on the aircraft. In these and other embodiments, based at least in part on the captured wing motion data, machine learning is employed to predict wing motion (e.g., normal, turbulent, and/or chaotic wing motion) of the aircraft.