Aircraft Flight Parameter Estimation for Sensor and Weight Fault Isolation
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Solution Overview
Problem
Current aircraft monitoring systems face challenges in distinguishing between sensor failures, weight errors, and common failure modes, particularly in distinguishing between angle of incidence and weight measurements, and existing estimators fail to address these issues effectively.
Innovation Solution
A method and device for monitoring and estimating aircraft flight parameters, including flight parameters, atmospheric parameters, sensor biases, and modeling biases, using an extended Kalman filter to iteratively determine parameter estimates and detect sensor statuses, including a detection module to identify failing sensors and weight errors, and a validation submodule to validate angle of incidence and weight statuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware redundancies with majority-based vote are used to monitor sensor failures, then sensor failure identification is improved, but device complexity increases and common failure modes cannot be detected
Solution Approach 1:
The patent introduces virtual sensors as intermediaries that compute estimated measurements from combinations of physical sensors through mathematical models. These virtual sensors act as mediators to detect failures without requiring additional physical sensors, thereby maintaining reliability while avoiding hardware complexity multiplication
Solution Approach 2:
The patent creates virtual copies of sensor measurements through analytical redundancy. Instead of duplicating physical sensors, mathematical models generate copies of measurement information from existing sensor data, enabling failure detection without increasing physical sensor count
2Reliability
If analytical redundancy with virtual sensors is used to increase flight parameter availability, then sensor failure compensation is improved, but measurement precision deteriorates due to assumption-based estimations
Solution Approach 1:
The patent implements feedback mechanisms where the monitoring system continuously compares virtual sensor estimates with actual physical sensor measurements. This feedback loop enables real-time validation of estimation assumptions and dynamic adjustment of monitoring strategies, improving both availability and precision
Solution Approach 2:
The patent employs dynamic monitoring strategies that adapt to changing flight conditions. The system adjusts which virtual sensors are active and how measurements are combined based on current flight parameters and sensor validity, optimizing precision while maintaining availability across varying operational states
3Productivity
If existing estimators are used to monitor flight parameters, then computation efficiency is improved, but difficulty of detecting and measuring increases due to inability to distinguish failure sources
Solution Approach 1:
The patent segments the monitoring function into distinct virtual sensors, each responsible for estimating specific flight parameters or detecting specific failure modes. This segmentation allows the system to identify which virtual sensor detects an anomaly, thereby locating the failure source without requiring complex computations across all sensors simultaneously
Solution Approach 2:
The patent applies local quality by assigning specific monitoring functions to specific virtual sensors based on their computational characteristics and sensor combinations. Each virtual sensor is optimized for its specific detection task, enabling efficient local failure detection that contributes to overall system-wide failure source identification
Data Source
AI summary
A device for monitoring and estimating parameters relating to the flight of an aircraft includes an estimation module 4 for determining an estimation of the values of the parameters relating to the flight of the aircraft and for generating residues, a detection module for determining the statuses associated with each of said sensors C1, C2, . . . , CN and with a parameter P1 corresponding to the weight of the aircraft, a transmission module for transmitting the statuses associated with each of said sensors C1, C2, . . . , CN to a user device and, on the next iteration, to the estimation module.