Aircraft Engine Deterioration Forecasting with Recurrent Neural Networks
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Solution Overview
Problem
Current maintenance scheduling for gas turbine engines struggles to predict and account for rapid deterioration caused by unknown combinations of factors, leading to unscheduled maintenance and disruptions in fleet operations.
Innovation Solution
A machine learning-based system using a recurrent neural network algorithm that fuses data from aircraft sensors, flight tracking, and environmental conditions to forecast engine deterioration, allowing for optimized maintenance scheduling and route adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional maintenance scheduling based on expected part life cycles is used, then maintenance planning is straightforward, but rapid deterioration from unknown factors causes unscheduled maintenance and operational disruptions
Solution Approach 1:
A recurrent neural network forecasting model serves as an intermediary between raw engine data and maintenance decisions. The model fuses multiple data sources (engine sensor data, flight tracking data, weather data) and transforms them into predictive deterioration forecasts, enabling proactive maintenance scheduling without requiring complex manual analysis of each data source
Solution Approach 2:
The forecasting model performs multiple functions: it processes diverse data types (sensor readings, flight parameters, weather conditions), identifies unknown deterioration causes, predicts time-to-failure, and supports maintenance scheduling decisions. This multi-functional approach consolidates what would otherwise require separate systems into a single unified platform
2Measurement precision
If more data sources are fused to improve prediction accuracy, then deterioration forecasting improves, but data processing complexity increases
Solution Approach 1:
The system merges multiple heterogeneous data sources (engine sensor data, flight tracking data, weather data) into a unified forecasting model. By combining these data streams at the model input level rather than processing them separately, the system achieves comprehensive deterioration prediction while avoiding the complexity of multiple separate processing pipelines
Solution Approach 2:
The patent replaces manual data analysis and traditional maintenance scheduling methods with an automated recurrent neural network system. The model automatically fuses data from multiple sources and generates predictions without requiring manual intervention to correlate different data types, significantly reducing processing complexity despite handling diverse data
Data Source
AI summary
A method for forecasting aircraft engine deterioration includes creating a first fused data set corresponding to a first actual aircraft engine. The first fused data set includes at least one as manufactured parameter of the actual aircraft engine, expected operating parameters of the first actual aircraft engine, and actual operating parameters of the actual aircraft engine. The actual operating parameters of the actual aircraft engine include internal aircraft sensor data, and external flight tracking data. The method further includes predicting an expected engine deterioration of the first actual engine based on the expected operating parameters and the actual operating parameters of the first actual aircraft engine by applying the first fused data set to a forecasting model. The forecasting model is a recurrent neural network based algorithm, and the recurrent neural network based algorithm is trained via a plurality of second fused data sets corresponding to actual aircraft engines.