Aircraft Engine Corrosion Risk Detection for Condition-Based Maintenance
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Solution Overview
Problem
Aircraft engines are susceptible to corrosion and erosion due to exposure to various environmental conditions, including rain, snow, sand, and salt ingestion, which current mitigation strategies may be either too conservative or insufficient, leading to increased costs or reliability issues.
Innovation Solution
A system and method using machine learning to analyze accumulated medium inside the aircraft engine, determining corrosion and erosion risks through a trained model, and initiating targeted mitigation actions based on usage data, including inspections, replacements, and engine washes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional conservative maintenance schedules are used, then engine reliability is maintained, but maintenance costs and downtime increase
Solution Approach 1:
The maintenance schedule transitions from static (fixed intervals) to dynamic (condition-based). The system continuously monitors engine conditions and adjusts maintenance timing based on actual degradation rates, allowing extensions between maintenance events when conditions are favorable while ensuring intervention when degradation accelerates.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the engine feeds into machine learning models that predict remaining useful life. These predictions trigger feedback to maintenance scheduling systems, which adjust future maintenance events based on actual engine behavior rather than following predetermined schedules.
2Reliability
If traditional conservative maintenance schedules are used, then engine reliability is maintained, but maintenance costs increase
Solution Approach 1:
The maintenance schedule transitions from static (fixed intervals) to dynamic (condition-based). The system continuously monitors engine conditions and adjusts maintenance timing based on actual degradation rates, allowing extensions between maintenance events when conditions are favorable while ensuring intervention when degradation accelerates.
Solution Approach 2:
The system changes the parameter basis for maintenance scheduling from time-based to condition-based parameters. By monitoring parameters such as vibration, temperature, and performance metrics, the system determines maintenance needs based on actual engine state rather than accumulated operating hours or calendar time.
3Loss of energy
If condition-based maintenance is implemented, then maintenance costs are reduced, but measurement precision requirements increase
Solution Approach 1:
The system employs multi-functional sensor arrays that simultaneously monitor multiple engine parameters (vibration, temperature, pressure, flow rates) using the same hardware infrastructure. This universal monitoring approach distributes the measurement precision burden across multiple parameters rather than requiring ultra-high precision on a single metric.
Solution Approach 2:
The system introduces machine learning models as intermediary layers between raw sensor data and maintenance decisions. These models aggregate information from multiple imperfect measurements, compensate for individual sensor limitations, and produce robust predictions that are more precise than any single measurement could achieve alone.
4Productivity
If machine learning models are used, then maintenance optimization is improved, but device complexity increases
Solution Approach 1:
The machine learning system is segmented into modular components: data acquisition modules, preprocessing modules, prediction models for different failure modes, and maintenance scheduling modules. Each component handles a specific aspect of the analysis, making the overall complex system manageable through clear separation of concerns and independent optimization of each segment.
Data Source
AI summary
Methods and systems for mitigating corrosion and/or erosion in an aircraft engine are provided. A method includes receiving usage data characterizing a medium accumulated inside the aircraft engine. Using a trained model, the usage data is related to an assigned aircraft engine condition from a plurality of aircraft engine conditions. The trained model is trained using machine learning and historical data relating characteristics of the medium to the plurality of aircraft engine conditions. When the assigned aircraft engine condition is indicative of a corrosion risk for the aircraft engine, a corrosion-mitigating action is initiated for the aircraft engine. When the assigned aircraft engine condition is indicative of an erosion risk for the aircraft engine, an erosion-mitigating action is initiated for the aircraft engine.