Aircraft Component Severity Prediction for Flight-by-Flight Maintenance
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Solution Overview
Problem
Existing reliability monitoring systems for aircraft components rely on fleet-based statistics, which are inadequate for optimizing maintenance and replacement decisions at the individual component level, particularly in military applications where operational readiness is critical, leading to sub-optimal part usage.
Innovation Solution
A physics-inspired neural network-based framework for flight-by-flight severity prediction that utilizes engine-specific data, including unique serial numbers and operational parameters, to provide precise and accurate forecasts for component maintenance and replacement, incorporating physics formulations with data-based models to account for non-linear deterioration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fleet-based 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-level to component-level predictions. It divides the monitoring approach into: (1) fleet-based statistical models for overall trends, and (2) physics-based deterioration models for individual component predictions. This segmentation allows each model to operate at its optimal level, with the physics-based model providing accurate individual component predictions without requiring extensive fleet data.
Solution Approach 2:
The patent introduces physics-based deterioration models as an intermediary between fleet statistics and individual component predictions. These models serve as a bridge that translates general fleet patterns into specific component-level forecasts by incorporating physical laws of deterioration, material science principles, and component-specific operating conditions, thereby achieving accurate individual predictions without relying solely on fleet data volume.
2Reliability
If parts are replaced early to ensure operational readiness, then reliability is improved, but component usage efficiency deteriorates
Solution Approach 1:
The patent enables preliminary assessment of component condition through physics-based deterioration modeling. By predicting future component states based on current degradation trends and physical laws, the system allows planners to schedule maintenance at the optimal moment - just before predicted failure - rather than using fixed early replacement schedules. This preliminary prediction capability ensures operational readiness while maximizing component utilization.
Solution Approach 2:
The patent transitions from static replacement schedules to dynamic, condition-based maintenance planning. The physics-based models continuously update component deterioration predictions based on actual operating conditions, allowing replacement timing to adapt dynamically to real-world usage patterns. This dynamic approach replaces components based on their actual condition and predicted future state rather than fixed time intervals, optimizing both reliability and usage efficiency.
3Ease of operation
If fleet-based performance data is used for maintenance forecasting, then data collection is simplified, but individual component prediction capability deteriorates
Solution Approach 1:
The patent applies local quality by tailoring the prediction approach to individual component characteristics. While fleet-based data collection remains simple, the physics-based deterioration models incorporate component-specific properties such as material composition, geometric features, operating conditions, and degradation mechanisms. This allows each component to be analyzed with appropriate local details while maintaining the simplicity of fleet-wide data gathering.
Data Source
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.