Aircraft Sensor Modeling for Real-Time Anomaly Detection
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Solution Overview
Problem
Current methods for detecting degraded components and subsystems in engineered systems, such as aircraft, are manual, time-consuming, and prone to false positives and missed detections, as they focus on individual subsystems without considering data from other components or external impacts.
Innovation Solution
A data-driven machine learning approach that trains predictive models using operational data from multiple components within an engineered system to distinguish between normal and anomalous behaviors, enabling detection of degraded components in the context of the entire system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert-based methods are used to detect degraded components, then detection accuracy may be maintained through expert knowledge, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces manual expert-based detection methods with an automated machine learning system that processes sensor data from multiple subsystems. The system uses trained models to automatically identify degraded components, eliminating the need for manual expert analysis while maintaining or improving detection accuracy through comprehensive data processing.
Solution Approach 2:
The machine learning system enables the aircraft maintenance system to self-diagnose degraded components by automatically analyzing sensor data from multiple subsystems. The trained models independently identify anomalies and predict failures without requiring continuous manual expert intervention, allowing the system to serve itself in the detection process.
2Device complexity
If subsystem-focused machine learning approaches are used, then processing complexity is reduced, but valuable latent information from other components and external impacts is missed
Solution Approach 1:
The patent merges data from multiple aircraft subsystems (engine, flight control, environmental systems) into a unified machine learning analysis framework. By combining sensor data across subsystem boundaries, the system captures latent information about component interactions and external environmental impacts that subsystem-focused approaches would miss, while the modular architecture manages processing complexity.
Solution Approach 2:
The machine learning system is designed with universal models that can analyze data from any aircraft subsystem and detect degraded components across the entire system. The same framework processes data from engines, flight controls, and environmental systems, enabling multi-functional detection capabilities that capture system-wide patterns and interactions.
3Ease of manufacture
If current machine learning techniques are applied to individual subsystems, then implementation simplicity is improved, but detection reliability decreases due to missed system-wide patterns
Solution Approach 1:
The patent segments the aircraft system into multiple data sources (different subsystems and sensors) while using a unified machine learning model to analyze them collectively. This segmentation approach maintains implementation simplicity by using standardized processing pipelines for each data source while improving reliability through the integration of multiple data streams that reveal system-wide degradation patterns.
Data Source
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AI summary
A system may include a set of components that make up an engineered system configured to generate real-time data representing a set of real-time operational behaviors associated respectively with the set of components. The system may include a predictive model configured to predict a normal operational behavior associated with a component of the set of components relative to other normal operational behaviors associated respectively with other components of the set of components. The system may include a processor configured to receive the set of real-time operational behaviors, the set of real-time operational behaviors including a real-time operational behavior associated with the component, and to categorize the real-time operational behavior associated with the component as normal or anomalous based on the predictive model. The system may include an output device configured to output an indication of fault in response to the processor categorizing the real-time operation behavior as anomalous.