Aircraft Brake Fault Detection via Torque and Temperature Discrepancies
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Solution Overview
Problem
Current aircraft braking system monitoring technologies are inadequate in detecting faults accurately, as they rely on individual sensor data, which can lead to undetectable faults due to active control systems compensating for deviations and require time-consuming maintenance investigations, often resulting in unnecessary interruptions.
Innovation Solution
A fault detection system that processes multiple sensor data types (braking demand, torque, wear, and temperature) to calculate expected values and determine significant discrepancies, generating alerts for specific fault types, including physical and monitoring-related faults, thereby enabling quicker fault identification and reducing operational interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor data types are processed to calculate expected values and determine discrepancies, then fault detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments fault detection into distinct analysis paths based on discrepancy types (temperature-related vs torque-related). The controller evaluates different sensor data combinations separately for different fault categories, allowing complex multi-sensor processing to be broken down into manageable, targeted analysis segments that improve accuracy without overwhelming system complexity
Solution Approach 2:
The system adds a new dimension of analysis by comparing actual sensor readings against calculated expected values derived from physics-based models. This creates a reference framework that transforms single-point measurements into multi-dimensional assessments, enabling more accurate fault detection while maintaining manageable complexity through structured comparison logic
2Measurement precision
If comprehensive investigations are performed to identify fault location and nature, then fault detection completeness is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary fault classification by comparing sensor data against expected values before maintenance personnel arrive. By pre-calculating discrepancy types (temperature-related, torque-related, or both) and generating specific alert codes, the system prepares diagnostic information in advance, significantly reducing the time required for on-site investigation while maintaining complete fault identification
Solution Approach 2:
The system provides immediate feedback through automated alerts that specify the type and location of detected faults. This feedback loop delivers diagnostic conclusions directly to operators without requiring manual investigation, reducing loss of time while maintaining detection completeness through continuous monitoring and automated analysis
3Productivity
If active control systems are used to optimize performance, then system performance is improved, but fault detectability worsens
Solution Approach 1:
The system introduces expected value calculations as an intermediary reference that bridges active control outputs and fault detection requirements. By comparing actual sensor readings against physics-based expected values rather than fixed thresholds, the system can distinguish between legitimate active control adjustments and actual faults, maintaining both optimized performance and accurate fault detectability
Solution Approach 2:
The system dynamically adjusts detection parameters by calculating expected values based on current operating conditions and wear states. This allows the fault detection thresholds to adapt to active control variations, maintaining sensitivity to actual faults while being tolerant of performance-optimizing control adjustments, thus preserving both productivity and fault detectability
4Device complexity
If individual sensor data is used for monitoring, then device complexity is reduced, but reliability of fault detection worsens
Solution Approach 1:
The system merges multiple sensor data types (temperature, torque, wear, wheel speed) into a unified fault detection analysis. By combining these data streams and comparing them against integrated expected value calculations, the system achieves reliable fault detection through multi-parameter correlation while maintaining relatively simple implementation through centralized controller processing
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
The system allows for more comprehensive fault detection and diagnosis, reducing aircraft downtime by identifying fault types and locations more efficiently, and potentially allowing continued operation until scheduled maintenance, avoiding unnecessary interruptions.
Implementation Method 1
receive torque information relating to an amount of torque reacted by the at least one brake during the braking event; receive brake temperature information relating to a maximum temperature of the at least one brake as a result of the braking event
Data Source
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AI summary
A fault detection system to detect faults relating to an aircraft braking system comprising a brake. The detection system comprises a controller configured to receive braking demand information; torque information; wear information; wheel speed information; and brake temperature information. The controller is to calculate an expected maximum temperature of the brake based on the torque information, the wear information and the wheel speed information; to determine whether a significant temperature discrepancy exists between the expected maximum temperature and the brake temperature information; to determine whether a significant torque-related discrepancy exists based on the braking demand information and the torque information. The controller is to generate a first alert if a significant torque-related discrepancy exists and no significant temperature discrepancy exists; a second alert if a significant temperature discrepancy exists and no significant torque-related discrepancy exists; and a third alert if a significant temperature discrepancy and a significant torque-related discrepancy exists.