Avionics System Predicting Aircraft Performance Using Aging Models
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Solution Overview
Problem
Conventional avionics warning systems are feedback-based and fail to account for component aging, limiting the ability to predict and address performance changes in aircraft during operation.
Innovation Solution
An avionics system that receives signals representing the current state of an aircraft and uses air performance models to calculate a future state, generating alerts to users about potential issues before they occur, thereby anticipating and mitigating performance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional feedback warning systems are used, then the system structure is simple, but the ability to predict future performance and account for component aging is lost
Solution Approach 1:
The system performs preliminary calculations of future component states using current sensor data and degradation models before actual failures occur. This allows the system to predict future performance and trigger warnings in advance, transitioning from reactive feedback to proactive prediction while maintaining manageable system complexity through algorithmic approaches.
2Measurement precision
If real-time monitoring of component aging is implemented, then the ability to detect performance changes is improved, but the complexity of the monitoring system increases
Solution Approach 1:
The system uses the aircraft's existing sensor network and computational resources to perform self-diagnosis and aging assessment. By leveraging already-deployed avionics infrastructure, the system achieves precise aging detection without adding substantial hardware complexity, as the monitoring function is integrated into the existing operational framework.
3Reliability
If component aging is accounted for in predictions, then the reliability of performance prediction is improved, but the computational requirements and system complexity increase
Solution Approach 1:
The system changes the parameters used in predictions by incorporating time-dependent degradation models that account for component aging. Instead of using static performance characteristics, the system dynamically adjusts prediction parameters based on accumulated operational data and estimated component life, improving reliability while managing computational load through efficient modeling approaches.
Data Source
AI summary
Methods and systems for operating an avionics system on-board an aircraft are provided. A plurality of signals representative of a current state of the aircraft are received. A future state of the aircraft is calculated based on the plurality of signals representative of the current state of the aircraft. An indication of the future state of the aircraft is generated with the avionics system on-board the aircraft.