On-board networked anomaly detection modules for aircraft
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Solution Overview
Problem
Detecting anomalies in aircraft systems is challenging due to the complexity of interactions between components, limited availability of sensors, difficulty in obtaining training data for anomalous behavior, and the need for real-time processing, which often results in misclassifications and costly maintenance activities.
Innovation Solution
A method involving the collection of sensor data from aircraft, retrieval of feature definitions, and use of an anomaly detection model to determine anomalous activity by comparing calculated feature values with patterns of normal values, including online networked anomaly detection modules that update learned test reference points and calculate convergence values to detect anomalies in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time anomaly detection is implemented using traditional methods, then detection speed is improved, but accuracy deteriorates due to misclassifications
Solution Approach 1:
The system segments anomaly detection into multiple specialized modules (e.g., sensor data module, flight data module, maintenance data module), where each module processes specific types of data independently. This segmentation allows parallel processing for speed while maintaining specialized accuracy for each data type, resolving the contradiction between real-time detection speed and accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data during flight operations and pre-training anomaly detection models using historical maintenance data before deployment. This preliminary preparation enables faster real-time detection without sacrificing accuracy, as the heavy computational work is done in advance when time is not critical.
2Reliability
If comprehensive sensor data collection is implemented, then detection coverage is improved, but system complexity deteriorates
Solution Approach 1:
The system implements a universal anomaly detection platform that handles multiple data types (sensor data, flight data, maintenance data) through a common architecture. This multi-functional approach provides comprehensive detection coverage across all aircraft systems while avoiding the complexity of separate specialized systems, as the core detection engine remains standardized and reusable across different data sources.
3Measurement precision
If extensive training data is collected for anomaly detection, then model accuracy is improved, but data acquisition difficulty deteriorates
Solution Approach 1:
The system creates synthetic copies of anomaly data by simulating fault conditions based on historical maintenance records and system models. Instead of requiring actual anomalous flight data (which is rare and difficult to obtain), the system generates representative training examples through copying and transforming normal operation data with injected fault patterns, thereby achieving high model accuracy without the difficulty of collecting extensive real anomaly data.
4Adaptability or versatility
If software updates are integrated with data elements, then system integration is improved, but certification complexity deteriorates
Solution Approach 1:
The system extracts and separates data elements from the software code structure, storing them as independent, versioned assets. This extraction allows software updates to be deployed independently from data updates, simplifying certification processes. The anomaly detection model can be retrained with new data without requiring software recertification, as the data layer is decoupled from the certified software layer, thereby reducing certification complexity while maintaining system integration.
Data Source
AI summary
Method and apparatus for detecting anomalous flights. Embodiments collect sensor data from a plurality of sensor devices onboard an aircraft during a flight. Feature definitions are determined, specifying a sensor device and an algorithm for deriving data values from sensor data collected from the device. Embodiments determine whether anomalous activity occurred during the flight using an anomaly detection model. An anomaly is detected including at least one of (i) a contextual anomaly where a data instance of a plurality of data instances is anomalous relative to a specific context, or (ii) a collective anomaly where two or more data instances are anomalous relative to a remainder of the plurality of data instances, even though each of the two or more data instances is not anomalous in and of itself. A report specifying a measure of the anomalous activity for the flight is generated.