Aircraft Engine Degradation Simulation Using Environmental Data
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Solution Overview
Problem
Conventional methods for predicting aircraft engine degradation are inaccurate due to the complexity of factors influencing engine wear, leading to over-estimation and unnecessary maintenance costs.
Innovation Solution
A system that utilizes machine learning models to simulate aircraft engine degradation based on environmental conditions and operational data, including air quality and flight plans, to provide precise predictions and optimize maintenance schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods over-estimate degradation to ensure safety, then reliability is improved, but manufacturing precision deteriorates due to unnecessary maintenance costs
Solution Approach 1:
The patent transforms the degradation prediction approach by changing from static conventional methods to dynamic machine learning models that process multiple variables including environmental conditions, operational parameters, and historical data. This parameter transformation enables accurate real-time degradation assessment without over-estimation, resolving the contradiction between safety and prediction accuracy
Solution Approach 2:
The patent replaces conventional mechanical estimation methods with intelligent machine learning systems that automatically analyze complex datasets. This substitution enables precise degradation prediction through algorithms that learn from historical patterns, eliminating the need for conservative over-estimation while maintaining safety standards
2Device complexity
If conventional methods use simple degradation models, then device complexity is reduced, but measurement precision deteriorates due to inability to capture complex degradation factors
Solution Approach 1:
The patent creates a universal machine learning platform that handles multiple degradation factors (environmental, operational, material) through a single integrated system. This multi-functional approach captures complex degradation mechanisms without proportionally increasing system complexity, as the ML model processes diverse inputs through unified algorithms
Solution Approach 2:
The machine learning model performs self-learning and automatic adaptation to capture complex degradation patterns without requiring manual model configuration for each factor. The system automatically identifies relationships between environmental conditions, operational parameters, and degradation outcomes, reducing the apparent complexity while improving measurement precision
Data Source
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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.