Aircraft Actuator Failure Detection and Biased Flight Replanning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle systems lack comprehensive situational awareness and robust control mechanisms, particularly in autonomous aircraft operations, leading to potential safety risks and inefficiencies due to limited sensor redundancy and pilot error detection capabilities.
Innovation Solution
A system and method that integrate multiple sensors and machine learning techniques to continuously assess aircraft conditions, provide situational awareness, and enable autonomous control by sampling inputs, determining correlations, and acting based on conditions, while also offering redundancy and contingency planning to ensure safe and reliable flight operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors and machine learning techniques are integrated to continuously assess aircraft conditions, then situational awareness and reliability are improved, but device complexity increases
Solution Approach 1:
The system divides aircraft monitoring into multiple independent sensor modules (accelerometers, gyroscopes, magnetometers, barometers) that each capture specific flight parameters. This segmentation allows the complex monitoring task to be distributed across simpler, specialized components, improving reliability through redundancy while managing overall system complexity.
Solution Approach 2:
The processor performs multiple functions: it processes data from various sensor types, detects pilot inputs, determines aircraft conditions, and generates control signals. This multi-functionality consolidates what would otherwise require separate systems into a single integrated unit, improving reliability without proportionally increasing complexity.
2Measurement precision
If sensor redundancy is improved through multiple sensors, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system employs multiple independent sensors (accelerometers, gyroscopes, magnetometers, barometers) that each measure specific flight parameters. This segmentation provides redundant measurement capabilities for each parameter type, improving measurement precision through cross-validation while keeping individual sensor complexity low.
Solution Approach 2:
The processor continuously compares data from multiple sensors to detect inconsistencies and validate measurements. This feedback mechanism ensures measurement precision by identifying and correcting sensor errors, while the automated nature of the process prevents complexity from escalating.
3Reliability
If the system provides comprehensive situational awareness and autonomous control, then safety is improved, but ease of operation decreases due to system complexity
Solution Approach 1:
The system autonomously monitors aircraft conditions, detects pilot inputs, determines flight conditions, and generates control signals without requiring manual intervention. This self-service capability improves safety by providing continuous automated oversight while simplifying operation for the pilot, who only needs to provide high-level guidance rather than manage complex monitoring tasks.
Solution Approach 2:
The system continuously feedbacks aircraft condition information to the pilot and automatically adjusts control signals based on detected conditions. This closed-loop feedback simplifies operation by handling complex decision-making automatically while keeping the pilot informed, thereby improving safety without sacrificing ease of operation.
Data Source
AI summary
An aircraft system of an aircraft may, while the aircraft is flying in a first flight mode, receive data from one or more sensors of the aircraft and, based on received data from one or more sensors, identify an actuation failure of an actuator of a flight control surface. The vehicle system may, subsequent to identifying the actuation failure of the actuator of the flight control surface, control the aircraft to fly in a modified flight mode. Controlling the aircraft to fly in the modified flight mode may include: determining a directional bias caused by the actuation failure, determining a modified flight plan based on the determined directional bias, and controlling the aircraft to fly based on the modified flight plan and the directional bias.