Flight-by-Flight Aircraft Component Distress Prediction
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Solution Overview
Problem
Existing reliability monitoring systems for aircraft components, particularly in military applications, fail to provide accurate predictive and preemptive maintenance insights at the individual component level due to reliance on fleet-wide statistics, leading to sub-optimal part usage and operational readiness.
Innovation Solution
A physics-inspired neural network-based framework for flight-by-flight severity prediction that utilizes engine-specific data, including TAC/EFH ratio, engine serial number, and other parameters, to model individual component deterioration and provide precise maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fleet-wide statistical models are used for reliability monitoring, then data requirements are reduced and system complexity is lowered, but prediction accuracy at the individual component level deteriorates
Solution Approach 1:
The patent segments the reliability monitoring system from fleet-wide to component-level predictions. It divides the monitoring approach into: (1) fleet-wide statistical models for general trends, and (2) individual component physics-based models for precise predictions. This segmentation allows each level to operate with appropriate complexity - simple statistical models for fleet overview and complex physics models for individual component accuracy.
Solution Approach 2:
The patent transitions from one-dimensional fleet-wide statistical aggregation to multi-dimensional component-level analysis by incorporating physics-based parameters (temperature, pressure, stress cycles, material properties) that add new dimensions to the prediction model. This dimensional expansion enables accurate individual component predictions without requiring complex fleet-wide data aggregation.
2Reliability
If parts are replaced early to ensure operational readiness, then reliability is improved, but component utilization efficiency deteriorates
Solution Approach 1:
The patent implements preliminary action by providing advance component-level health predictions that enable planned maintenance scheduling. Instead of reactive replacement or overly conservative early replacement, the system predicts component deterioration trends and schedules maintenance just before predicted failure points, optimizing both reliability and utilization.
Solution Approach 2:
The patent changes the decision parameter from fixed replacement schedules to dynamic, condition-based predictions. By using physics-based models that track actual component degradation parameters (temperature exposure, stress cycles, pressure differentials), the system adjusts maintenance timing based on real component state rather than predetermined intervals, maximizing utilization while ensuring reliability.
3Measurement precision
If component-level predictive monitoring is implemented, then prediction accuracy is improved, but data requirements and computational complexity increase
Solution Approach 1:
The patent introduces physics-based models as intermediaries between raw sensor data and prediction outcomes. These models act as mediators that translate component operating conditions (temperature, pressure, flow rates) into meaningful degradation predictions using established physical relationships. This intermediary layer reduces the need for massive datasets by leveraging physics principles that inherently encode failure mechanisms.
Solution Approach 2:
The patent replaces data-intensive statistical learning approaches with physics-based computational models. Instead of requiring large datasets to train machine learning algorithms, the system uses fundamental physics equations (thermodynamics, fluid mechanics, material science) to directly compute component degradation, significantly reducing data requirements while maintaining or improving prediction accuracy.
Data Source
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AI summary
There are provided systems and methods for prognostic analytics of an asset. For example, there is provided a processor-implemented method for severity prediction for aircraft components. The method includes accessing time series flight-by-flight data relating to a component of an aircraft, the time series flight-by-flight data comprising performance data; determining, by a prediction model, an estimated degree of distress for the component based on the time series flight-by-flight data; determining a flight-by-flight severity prediction for the component based on the estimated degree of distress; and providing a preemptive recommendation for the component based on determined the flight-by-flight severity prediction.