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, or environmental exposure, leading to potential inaccuracies in reporting and increased inventory costs for airlines.
Innovation Solution
A prognostic health monitoring (PHM) system that includes a PHM data coordinator sampling raw AoA data and sensor health data, determining responsiveness factors, and generating alerts for maintenance personnel when thresholds are exceeded, identifying specific fault conditions in AoA sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AoA sensors are exposed to harsh environmental conditions (cold air, moisture, contaminants, bird strikes, debris, icing), then they can perform their function of measuring angle of attack, but they sustain deformation or damage and cannot self-identify faults
Solution Approach 1:
The patent implements self-service by enabling the AoA sensor to automatically monitor its own health parameters (voltage, current, temperature, accelerometer data) and self-identify faults without external intervention. The sensor performs self-diagnostics by comparing its operational parameters against expected ranges and generates fault indicators when anomalies are detected, allowing the sensor to service itself rather than requiring external monitoring systems.
Solution Approach 2:
The patent applies feedback by continuously monitoring the sensor's operational parameters (voltage, current, temperature, acceleration) and using this information to determine sensor health status. The system feeds back this health information to the aircraft's flight management system, enabling real-time detection of sensor degradation or failure conditions based on deviations from normal operational ranges.
2Reliability
If heater wires or heating elements are incorporated into the AoA sensor to prevent ice formation, then ice formation is prevented, but prolonged usage and frequent on/off switching lead to unexpected breakdown of the heating system
Solution Approach 1:
The patent uses feedback by monitoring the heating element's electrical parameters (voltage, current) and thermal state (temperature) to detect degradation or failure conditions. The system continuously compares these parameters against expected ranges and can identify when the heating element is malfunctioning, allowing for proactive replacement before complete failure occurs, thus extending the effective operational lifespan of the heating system.
Solution Approach 2:
The patent applies preliminary action by detecting early signs of heating element degradation through monitoring of electrical and thermal parameters before complete failure occurs. The system can identify trends such as increasing power consumption or temperature deviations that indicate impending failure, allowing maintenance personnel to replace the heating element proactively rather than reactively after breakdown.
3Reliability
If multiple redundant AoA sensors are incorporated into the aircraft, then redundancy is achieved, but inventory costs increase for airlines to replace faulty sensors promptly
Solution Approach 1:
The patent applies feedback by providing continuous health status information for each AoA sensor to the aircraft's flight management system and maintenance personnel. This real-time feedback on sensor health (through monitoring of voltage, current, temperature, and accelerometer parameters) allows airlines to precisely identify which specific sensor has degraded or failed, enabling targeted replacement rather than blanket replacement of all redundant sensors, thus reducing inventory requirements.
Solution Approach 2:
The patent uses an intermediary approach by introducing a monitoring system that acts as a mediator between the redundant sensors and the flight management system. This intermediary monitoring layer provides detailed health information about each sensor, enabling intelligent decision-making about which sensors need replacement and when, thereby optimizing inventory management across the fleet.
4Measurement precision
If the air data computer logs faulty AoA sensors for replacement on-ground, then faulty sensors are identified, but the cause or nature of the faulty performance cannot be identified
Solution Approach 1:
The patent applies feedback by continuously monitoring multiple sensor parameters (voltage, current, temperature, accelerometer data) and providing detailed information about the sensor's operational state. When a fault is detected, the system provides feedback not just that a fault exists, but specifically what type of fault based on which parameters are abnormal, thereby preserving information about the fault's nature and cause rather than merely logging that a fault occurred.
Solution Approach 2:
The patent introduces an intermediary monitoring system that sits between the AoA sensor and the flight management computer, extracting and analyzing detailed health information from the sensor. This intermediary layer translates raw sensor data into meaningful fault diagnostics, identifying whether the fault is due to electrical issues, mechanical damage, icing, or other causes, thereby preventing loss of diagnostic information.
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
Enables early detection of sensor faults, reducing operational disruptions and inventory costs by predicting imminent failures and allowing for proactive maintenance.
Implementation Method 1
Heater wires or other heating elements and systems (e.g., positive temperature coefficient (PTR) heater elements and/or heater packs) may be incorporated into the AoA sensor, providing an operational voltage to heat the sensor and/or probes, preventing ice formation.
Data Source
AI summary
A device or system for prognostic health monitoring (PHM) of aircraft-based analog angle of attack (AoA) sensors includes a data coordinator for sampling AoA data collected by one or more AoA sensors. The data coordinator concurrently samples sensor health data from a suite of monitoring sensors within each AoA sensor (e.g., orientation, voltage, current, temperature). Based on the analog AoA data and sensor health data, the data coordinator determines a sensor responsiveness factor (RF) for each reporting analog AoA sensor. Current RF data is compared to historical RF data to determine a trending responsiveness factor for each analog 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.