Aircraft Operation Forecasting for Predictive Maintenance Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting aircraft engine component failures lack accuracy in capturing non-linearity and seasonality, leading to inefficient maintenance scheduling and inventory management.
Innovation Solution
A method utilizing a Hidden Markov Model (HMM) and bootstrapping procedure to forecast aircraft operational data, building a transition probability matrix from historical flight data and applying it to predict future sensor parameters, ensuring accurate maintenance scheduling and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear prediction methods are used for aircraft component failure prediction, then the prediction process is simple, but the accuracy is insufficient due to inability to capture non-linearity and seasonality
Solution Approach 1:
The patent applies dynamics by transitioning from static linear prediction models to dynamic models that adapt to changing operational conditions. The system uses dynamic factor weighting and time-varying parameters to capture the non-linear and seasonal characteristics of aircraft component degradation, allowing the prediction model to evolve with actual operational data.
Solution Approach 2:
The patent implements parameter changes by introducing multiple varying parameters including seasonal factors, operational intensity variables, and degradation rate coefficients. These parameters are continuously adjusted based on real-time sensor data and historical patterns, enabling the model to adapt to different flight conditions, seasons, and component states.
2Reliability
If accurate prediction of component failures is achieved through complex models, then maintenance reliability improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing operational data to extract key features and pre-calculating baseline degradation patterns. Historical data is analyzed in advance to establish reference models and seasonal patterns, so that real-time prediction requires only incremental updates rather than complete re-computation, reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent implements segmentation by dividing the prediction process into modular components: data acquisition modules, feature extraction modules, prediction modules for different component types, and maintenance scheduling modules. This segmentation allows parallel processing of multiple components and enables the system to focus computational resources on high-priority predictions.
Data Source
AI summary
A method of forecasting operation data of an aircraft includes receiving, by a computer system, historical flight data of the aircraft, the historical flight data including historical departure and arrival airports, and historical period of flight occurrence, calculating a transition probability matrix based on the historical flight data, determining, based on the transition probability, using a hidden Markov model (HMM), forecasted arrival airports, and time of arrival of the aircraft to the arrival airports to build a forecasted sequence of future routes, receiving, past aircraft sensor parameters, calculating, using a bootstrapping procedure based on the past aircraft sensor parameters and the forecasted sequence of future routes, forecast future aircraft operational data, and determining, based on the forecast future aircraft operational data, a maintenance schedule of one or more systems of the aircraft to prevent failure of the one or more system.