Real-Time Oil Consumption Detection in Aircraft Engines
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Solution Overview
Problem
Real-time oil consumption monitoring in aircrafts is hindered by limited sensor precision, oil motion during maneuvers, and the effects of oil 'gulping' due to changes in engine power demands, leading to reduced accuracy and increased false alarms during transient operations.
Innovation Solution
A system comprising an oil quantity sensor and a processor that calculates a corrected oil quantity by applying engine operational and environmental conditions, predicts oil quantity using models, calculates a prediction error, and determines oil consumption rate using a regression algorithm, while weighting the consumption rate inversely proportional to aircraft stability to improve accuracy and reduce sensitivity during transient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If modeling is used to accommodate sensor limitations and oil motion effects, then measurement precision is improved during stable operation, but reliability deteriorates during transient operation due to reduced correction quality
Solution Approach 1:
The system dynamically adjusts the detection threshold based on operational conditions. During transient operations, the threshold is adjusted to account for model limitations, while during stable operations, the threshold is set for high precision detection. This dynamic adaptation resolves the contradiction by optimizing detection parameters according to the operational phase.
Solution Approach 2:
The system changes the detection parameters (threshold values, sensitivity settings) based on the operational state. When transient conditions are detected, the system modifies the correction quality parameters and threshold levels to maintain reliability. This parameter adaptation allows the system to balance precision and reliability across different operational modes.
2Measurement precision
If high sensitivity is used during normal operation to detect oil consumption, then detection precision is improved, but false alarms increase during transient operation
Solution Approach 1:
The detection threshold is dynamically adjusted based on the operational state. During transient operations, the threshold is raised to prevent false alarms, while during stable operations, it is lowered to enable sensitive detection of actual oil consumption. This dynamic thresholding resolves the contradiction between detection precision and false alarm reduction.
Solution Approach 2:
The system uses feedback from operational sensors to continuously monitor the operational state and adjust the detection threshold accordingly. When transient conditions are detected through feedback from engine parameters and oil motion sensors, the system automatically adjusts the threshold to prevent false alarms while maintaining detection capability.
3Reliability
If reduced sensitivity is used during transient operation to reduce false alarms, then reliability is improved, but detection precision is reduced during normal operation
Solution Approach 1:
The system dynamically switches between high-sensitivity and low-sensitivity detection modes based on the operational state. During normal stable operation, high sensitivity is enabled for precise oil consumption detection. During transient operation, the system automatically reduces sensitivity to maintain reliability. This dynamic mode switching resolves the contradiction by optimizing detection parameters for each operational phase.
Data Source
AI summary
A method of real-time oil consumption is disclosed. A method of real-time oil consumption detection may include capturing a raw oil quantity, calculating a corrected oil quantity, calculating a predicted oil quantity, calculating a prediction error, and calculating an estimated oil consumption rate. Raw oil quantity may be captured from an oil quantity sensor in an engine. Corrected oil quantity may be calculated by taking raw oil quantity and applying environmental and engine operational conditions. Prediction error may be calculated by finding the difference between corrected oil quantity and predicted oil quantity. Oil consumption rate may be calculated by applying a regression algorithm to prediction error.