Dissimilar Air Data Sensor Voting for Common-Cause Failure Isolation
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Solution Overview
Problem
Existing aircraft systems face challenges in providing reliable air data due to external threats like bird strikes and icing, leading to unreliable measurements that can compromise flight safety, as redundancy mechanisms fail to address common-cause errors effectively.
Innovation Solution
Implementing a system with dissimilar air data sensors of different types, such as multi-function probes, flush-mounted pressure sensors, and angle of attack vanes, which are configured to output dissimilar data, and a flight control unit that performs voting and correction algorithms to ensure accurate air data reception, even in the presence of common threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple similar air data sensors are used for redundancy, then reliability is improved, but vulnerability to common-cause events (icing, bird strikes) worsens
Solution Approach 1:
The system segments air data sensing into multiple dissimilar sensor types (Pitot-static, flush-mounted, multi-function probes) rather than using identical sensors. This segmentation by sensor type diversity ensures that a common threat affecting one sensor type does not necessarily affect all sensors, thereby maintaining reliability while reducing vulnerability to common-cause events.
Solution Approach 2:
The system employs asymmetric sensor configuration with different sensor types having different physical characteristics and threat vulnerabilities. For example, Pitot-static sensors have different icing characteristics compared to flush-mounted sensors, creating an asymmetric response to threats that allows the system to maintain at least one functional sensor type during common-cause events.
2Measurement precision
If all air data sensors are corrupted by external threats, then measurement accuracy deteriorates, but system complexity increases when implementing dissimilar sensors
Solution Approach 1:
The system uses multi-function probes that can measure multiple air data parameters (pressure, temperature, angle of attack) with a single sensor unit. This multi-functionality reduces the overall number of sensors needed while maintaining measurement precision, thereby managing system complexity despite the use of dissimilar sensor types.
Solution Approach 2:
The flight control unit implements continuous monitoring and voting logic that compares readings from dissimilar sensors. When sensor corruption is detected through inconsistent readings, the system provides feedback to isolate and exclude corrupted sensor data, maintaining measurement precision while managing complexity through automated fault detection and exclusion.
3Reliability
If voting logic is implemented to select reliable data, then reliability is improved, but processing complexity increases
Solution Approach 1:
The voting logic implements partial action by only processing and comparing data from sensors that pass initial consistency checks. Rather than exhaustively analyzing all possible sensor combinations, the system applies voting logic selectively to subsets of sensors, reducing processing complexity while maintaining reliability through targeted validation of critical air data parameters.
Data Source
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Figure 2
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AI summary
A system and a method include air data sensors configured to detect one more characteristics of air surrounding an aircraft. At least three of the air data sensors differ in type. The air data sensors are configured to output air data. A flight control unit is in communication with the air data sensors. The flight control unit is configured to receive the air data from the air data sensors and control at least one aspect of the aircraft based on at least a portion of the air data. In at least one example, the flight control unit is further configured to vote in relation to the air data from the air data sensors.