Autoencoder Anomaly Detection for In-Flight Alert Automation
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Solution Overview
Problem
Current aircraft tracking systems rely on manual reporting of anomalies, which can be inefficient and delayed, especially in situations like inclement weather, where automated and timely detection of deviations from intended flight paths is crucial for safety and operational efficiency.
Innovation Solution
A machine learning-based method that processes flight data using autoencoders and anomaly models to generate alerts for potential deviations, such as diversions or holding patterns, by analyzing longitude, latitude, altitude, and ground speed data, and interpolating missing records to provide real-time anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual reporting of anomalies is used, then human judgment and context understanding are available, but detection speed and automation are reduced
Solution Approach 1:
The patent replaces manual mechanical monitoring systems with an automated machine learning-based detection system. The autoencoder model automatically processes flight data, identifies anomalies, and generates alerts without human intervention, thereby increasing automation extent while reducing detection time through continuous automated monitoring.
Solution Approach 2:
The system performs self-service by automatically detecting and reporting its own anomalies through the machine learning model. The autoencoder continuously monitors flight data, identifies deviations from normal patterns, and generates alerts autonomously, eliminating the need for manual analysis while maintaining high detection speed.
2Productivity
If manual anomaly identification is used, then contextual understanding is available, but productivity and response speed are reduced
Solution Approach 1:
The machine learning model enables continuous anomaly detection by continuously processing flight data streams without interruption. The autoencoder operates continuously to monitor aircraft position, altitude, and other parameters, ensuring that anomalies are detected immediately upon occurrence rather than through periodic manual checks, thereby increasing productivity and reducing response time.
Solution Approach 2:
The patent substitutes manual productivity-constraining processes with automated machine learning algorithms that can process vast amounts of flight data rapidly. The system automatically identifies anomalies and generates alerts instantly, dramatically improving detection productivity and reducing the time between anomaly occurrence and response.
3Speed
If automated detection systems are implemented, then detection speed is improved, but system complexity increases
Solution Approach 1:
The patent extracts the core anomaly detection functionality into a specialized machine learning model (autoencoder) that can be independently deployed and scaled. By separating the detection logic from the overall system architecture, the system achieves high detection speed while managing complexity through modular design, where the autoencoder handles the complex pattern recognition separately from data collection and alert generation components.
Data Source
AI summary
The present disclosure provides techniques for machine learning-based anomaly prediction. A set of flight data for a flight of an aircraft is accessed, and an embedding is generated by processing the set of flight data using an autoencoder machine learning model. A reconstruction error is generated based on the embedding using the autoencoder machine learning model. An anomaly measure is generated for the set of flight data by processing the embedding and the reconstruction error using an anomaly machine learning model. In response to determining that the anomaly measure satisfies one or more criteria, an alert is output.