Aircraft AoA Sensor Health Monitoring for Fault Trend Detection
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Solution Overview
Problem
Current aircraft-based angle of attack (AoA) sensors are unable to self-identify and self-isolate sensor faults due to wear, damage, deformation, or environmental exposure, leading to inaccurate readings that can endanger the aircraft and its crew, and require costly and unpredictable maintenance.
Innovation Solution
A prognostic health monitoring (PHM) system is implemented within each AoA sensor, incorporating onboard monitoring sensors and a data analyzer that tracks sensor responsiveness trends, generating alerts for maintenance when thresholds are exceeded, and identifying specific fault conditions such as underdamping, overdamping, or heating element failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple AoA sensors are incorporated to ensure redundancy and safety, then the reliability of the aircraft's angle of attack measurement is improved, but the device complexity and maintenance burden increase
Solution Approach 1:
The patent implements self-diagnosis functionality within each AoA sensor that enables the sensor to automatically detect its own operational status, identify faults, and report to the air data computer without requiring external inspection. This self-service capability allows the system to monitor the health of multiple sensors independently, maintaining redundancy while reducing the complexity of manual monitoring and maintenance scheduling.
2Measurement precision
If AoA sensors are exposed to harsh environmental conditions to maintain accurate measurements, then the measurement precision is improved, but the sensors become more susceptible to damage and failure
Solution Approach 1:
The patent incorporates monitoring sensors that continuously assess the operational status of AoA sensors before failures occur. These sensors detect early signs of damage from environmental factors such as moisture, contaminants, bird strikes, or debris, allowing the system to identify and isolate compromised sensors proactively. This preliminary detection enables maintenance to be performed before the harsh environment causes complete sensor failure, preserving measurement precision.
3Measurement precision
If heating elements are used to prevent ice formation on probes, then the measurement accuracy is maintained in cold conditions, but the heating system may break down due to prolonged usage
Solution Approach 1:
The patent incorporates monitoring sensors that continuously assess the operational status of heating elements within AoA sensors. These sensors detect changes in electrical characteristics, temperature deviations, or other indicators of heating system degradation, providing real-time feedback to the air data computer. This feedback mechanism allows the system to identify failing heating elements before they cause complete loss of ice protection, enabling proactive maintenance to preserve measurement accuracy in cold conditions.
4Reliability
If AoA sensors are replaced after unknown faults occur, then the safety risk is minimized, but the maintenance cost and operational disruption increase
Solution Approach 1:
The patent implements self-diagnosis functionality that enables AoA sensors to automatically detect and report their own operational status, including identification of specific faults such as sensor degradation, heating element failure, or probe damage. This self-service capability provides advance notice of potential failures, allowing maintenance personnel to schedule replacements proactively during planned downtime rather than reacting to sudden failures, thereby reducing unscheduled maintenance time and operational disruption while maintaining safety.
Data Source
AI summary
A device or system for prognostic health monitoring (PHM) of aircraft-based digital angle of attack (AoA) sensors includes a data concentrator within each AoA sensor for sampling raw AoA data collected by that sensor. A PHM data analyzer receives the sampled AoA data along with sensor health data from monitoring sensors within each AoA sensor (e.g., orientation, voltage, current, temperature). Based on the AoA data and sensor health data, the PHM analyzer determines a sensor responsiveness factor (RF) for each reporting AoA sensor. Current RF data is compared to historical RF data to determine a trending responsiveness factor for each AoA sensor. If the responsiveness factor for any AoA sensor trends beyond a threshold level, the data coordinator generates an alert for preventive maintenance personal on the ground, the alert indicative of one or more specific fault conditions in that AoA sensor depending on the responsiveness threshold breached.