Vehicle Structural Anomaly Detection Using Persistent Model Errors
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Solution Overview
Problem
Vehicle structures, such as those in aircraft, face challenges in detecting and mitigating structural anomalies in real-time due to exogenous forces, which can lead to non-optimal conditions and reduced operational health, with existing systems not effectively addressing the persistence and significance of modeling errors in structural behavior.
Innovation Solution
A real-time model-based system that receives measurements from vehicle structures, compares them to expected data to generate a modeling error signal, calculates statistical significance, and determines persistence to indicate structural anomalies, activating a control mechanism to compensate for anomalies through actuation of control surfaces, flow control, or active structural materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time measurements are compared with expected operation data to detect structural anomalies, then the detection capability is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces a nominal model of structural behavior as an intermediary between the complex structural system and the detection process. The model serves as a mediator that translates complex structural responses into comparable predictions, enabling anomaly detection without requiring direct complex analysis of the entire structure. The model-based approach acts as a mediator that simplifies the comparison process while maintaining detection accuracy.
Solution Approach 2:
The system continuously compares real-time measurements with model predictions and uses the resulting error signals to update the anomaly detection process. This feedback mechanism enables real-time monitoring where the system constantly adjusts its assessment based on the difference between expected and actual behavior, improving detection capability through iterative comparison rather than complex one-time analysis.
2Reliability
If statistical analysis is performed on modeling errors to determine anomaly significance, then the reliability of anomaly indication is improved, but the computational time increases
Solution Approach 1:
The patent pre-calculates and stores statistical parameters (mean and standard deviation) of the modeling error signal during normal operation. By preparing these statistical benchmarks in advance, the system can quickly assess anomaly significance during real-time operation without performing complex statistical analysis from scratch, thus maintaining high reliability while reducing computational time during critical detection phases.
Solution Approach 2:
The system changes the approach from complex real-time statistical analysis to comparing against pre-computed statistical parameters. By transforming the problem from calculating full statistical distributions in real-time to comparing against stored mean and standard deviation values, the system maintains reliable anomaly detection while significantly reducing computational burden and time requirements.
3Duration of action of stationary object
If control mechanisms are activated to compensate for structural anomalies, then the structural life is prolonged, but the device complexity increases
Solution Approach 1:
The system employs active structural materials and control mechanisms that automatically respond to detected anomalies without requiring external intervention. The structure essentially serves itself by using embedded sensors, actuators, and control algorithms to detect and compensate for anomalies in real-time, prolonging structural life through self-monitoring and self-correction capabilities that integrate seamlessly into the existing structure rather than adding separate complex control systems.
Data Source
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AI summary
A system and methods for real-time model based vehicle structural anomaly detection are disclosed. A real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle is received, and the real-time measurement is compared to expected operation data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated, if the persistence exceeds a persistence threshold value.