Aircraft Engine Degradation Simulation Using Airport Environmental Data
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Solution Overview
Problem
Conventional methods for predicting aircraft engine degradation often overestimate degradation to ensure safety, leading to unnecessary costs due to their inability to accurately account for complex factors like engine use, environmental conditions, and material aging.
Innovation Solution
A method involving the use of machine learning models to simulate aircraft engine degradation based on air quality and operational data, including air quality index (AQI) at source and destination airports, engine exhaust gas temperature, and thrust rating, to provide precise predictions of component degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods over-estimate degradation to ensure safety, then reliability is improved, but cost increases due to unnecessary maintenance
Solution Approach 1:
The patent changes the parameters used in degradation prediction from generic conservative estimates to specific environmental parameters (air quality index, temperature, humidity) and operational parameters (flight hours, cycles, altitude). This allows the system to calculate actual degradation based on real conditions rather than over-estimating with fixed safety buffers, thereby reducing unnecessary maintenance costs while maintaining reliability.
Solution Approach 2:
The patent replaces conventional mechanical/empirical degradation estimation methods with machine learning models that process environmental and operational data. This substitution enables more accurate prediction of actual degradation patterns, eliminating the need for conservative over-estimation and reducing unnecessary maintenance interventions.
2Ease of operation
If conventional methods use simple degradation models, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediaries between raw environmental/operational data and degradation predictions. These models automatically process complex relationships between multiple parameters (air quality, temperature, flight conditions) without requiring manual intervention, maintaining ease of operation while dramatically improving prediction precision through data-driven insights.
Solution Approach 2:
The patent creates a universal prediction system that handles multiple degradation factors (environmental conditions, operational stress, material aging) through a single integrated machine learning framework. This multi-functional approach maintains operational simplicity while achieving comprehensive and precise degradation measurement across diverse flight conditions.
Data Source
AI summary
The present disclosure provides techniques for dynamic utilization of aircraft based on environmental conditions. A proposed flight plan for an aircraft is received. Environment data representing a set of environmental conditions at a source airport indicated in the proposed flight plan is collected. Weather data representing a set of environmental conditions at a destination airport indicated in the proposed flight plan is collected. Operation data related to the aircraft indicated in the proposed flight plan is received. Aircraft engine degradation of the aircraft is dynamically simulated based on the collected environment data and the received operation data using a trained machine learning (ML) model. The simulated aircraft engine degradation is output.