Aircraft Fault Alerting Using Flight Prediction Aggregation
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Solution Overview
Problem
Existing predictive models for aircraft component faults provide inconsistent flight-level predictions, leading to misleading metrics and challenges in interpreting individual predictions for timely maintenance actions, often relying on biased human expertise and resulting in scattered false positives.
Innovation Solution
A method that aggregates flight-level predictions using a predictive model, selecting settings based on a tolerance of false alerts, and determining optimal detection and alert group settings to generate improved maintenance alerts, incorporating inspection data to minimize false alerts and enhance predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If flight-level predictions are generated using a predictive model, then fault prediction capability is improved, but false positives increase and interpretation becomes challenging
Solution Approach 1:
The patent combines multiple flight-level predictions into aggregated maintenance alerts by merging predictions across multiple flights and components. This aggregation process consolidates scattered false positives into unified alert signals, improving overall prediction reliability while maintaining interpretability through structured alert categories.
Solution Approach 2:
The patent introduces an intermediary alert generation layer between raw flight-level predictions and final maintenance decisions. This intermediary layer processes individual flight predictions through detection windows and alert rules, transforming inconsistent flight-level data into reliable maintenance alerts that reduce false positives while preserving prediction capability.
2Loss of information
If individual flight-level predictions are analyzed, then detailed fault information is obtained, but interpretation complexity and false positives increase
Solution Approach 1:
The patent merges individual flight-level predictions into aggregated maintenance alerts that preserve essential fault information while eliminating interpretation complexity. By combining multiple predictions and applying alert rules, the system maintains detailed fault information through structured alert data while presenting simplified interpretation through unified alert signals.
3Loss of time
If maintenance alerts are generated based on flight-level predictions, then timely maintenance actions are enabled, but false alerts increase operational complexity
Solution Approach 1:
The patent implements feedback mechanisms where alert performance is continuously monitored and used to refine detection windows and alert rules. This feedback loop reduces false alerts over time while maintaining timely maintenance responses, as the system learns from operational data to optimize alert generation parameters and minimize harmful false positives.
Solution Approach 2:
The patent applies preliminary filtering through detection windows and alert rules before generating maintenance alerts. This preliminary action processes flight-level predictions through structured criteria, enabling timely maintenance actions while reducing false alerts by pre-filtering predictions that do not meet alert thresholds or patterns.
4Productivity
If predictive models provide flight-level predictions, then maintenance planning is improved, but scattered false positives reduce reliability
Solution Approach 1:
The patent merges scattered flight-level predictions into consolidated maintenance alerts, improving prediction consistency while maintaining planning efficiency. By aggregating predictions across flights and applying unified alert rules, the system eliminates scattered false positives and provides reliable, consistent maintenance planning information.
Data Source
AI summary
The present disclosure provides a method including: receiving flight sensor data and component fault data corresponding to a plurality of flights; applying a predictive model to individual flights of the plurality of flights to generate a plurality of fault probabilities for at least one aircraft component; selecting, based on a factor indicating a tolerance of false alerts, a plurality of settings comprising a minimum count of flights, a threshold probability, one or more detection window settings, and one or more alert group settings; detecting, based on the one or more detection window settings, a condition that at least the minimum count of flights within a detection window have a respective fault probability, of the plurality of fault probabilities, that is greater than the threshold probability; and determining, based on the one or more alert group settings, whether to generate a new alert for the condition.