Aircraft Engine Redundant Sensor Mismatch Selection Using AI
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Solution Overview
Problem
Modern aircraft electronic control systems lack the capability to establish the integrity and validity of redundant sensor signals, leading to uncertainty in selecting accurate data when mismatch errors occur between redundant sensors, which can result in non-optimal engine performance and reduced efficiency.
Innovation Solution
The implementation of an artificial intelligence (AI) model with a database of parameter values, integrated into an engine data recorder (EDR), which identifies mismatched parameter values from redundant sensors and produces a predicted value to assist the control unit in selecting the appropriate sensor data for engine control, utilizing historical and operational data for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple redundant sensors are used to sense the same parameter, then system reliability is improved, but device complexity and cost increase
Solution Approach 1:
An AI model serves as an intermediary between the redundant sensors and the control system. The AI model receives signals from multiple redundant sensors, analyzes them to detect mismatch errors, and outputs a determined parameter value. This intermediary resolves the complexity of handling multiple sensors while maintaining high reliability through intelligent signal evaluation.
2Reliability
If redundant sensors are used to provide backup signals, then failure mitigation is improved, but the ability to detect and resolve mismatch errors deteriorates
Solution Approach 1:
The patent replaces traditional mechanical or rule-based sensor evaluation methods with an AI-based system. The AI model analyzes sensor signals to detect mismatch errors that would be difficult to identify using conventional threshold-based or simple redundancy logic. This substitution enables sophisticated detection and resolution of mismatch errors while maintaining failure mitigation capabilities.
3Ease of operation
If traditional control logic is used to handle sensor redundancy, then system simplicity is maintained, but the ability to confidently select accurate sensor data deteriorates
Solution Approach 1:
The AI model changes the approach from simple parameter selection to intelligent parameter determination. Instead of using fixed rules or arbitrary selection methods, the AI model dynamically evaluates sensor signals based on learned patterns and operational contexts, determining the most accurate parameter value even when sensors disagree. This maintains operational simplicity while dramatically improving measurement precision.
Data Source
AI summary
A method and system for processing parameter values from a redundant sensor configured to sense a parameter used in the control of an aircraft engine is provided. The method includes: a) receiving a plurality of parameter values from a redundant sensor by sensing the same parameter at the same time; b) identifying mismatched parameter values; c) producing a predicted parameter value using an artificial intelligence (AI) model having a database of parameter values representative of the sensed parameter; d) providing the predicted parameter value to a control unit; and e) operating the control unit to select a first parameter value or a second parameter value using the predicted parameter for use in the control of the aircraft engine.