Fuel Metering Anomaly Detection via Nominal Response Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern electronic systems, such as avionics, face challenges in detecting and isolating operational issues due to their integrated nature, making it difficult to ensure correct functioning.
Innovation Solution
A method for detecting anomalies in fuel demand and supply data by generating a nominal response model and comparing it with collected data, using a statistical bootstrap technique to identify and isolate operational issues in a fuel metering system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple subcomponents are integrated into a fuel metering system, then system functionality is improved, but difficulty in detecting and isolating operational issues increases
Solution Approach 1:
The patent segments the fuel metering system into distinct subcomponents (fuel demand sensor, fuel supply sensor, controller, actuator, valves) and analyzes each independently using single-input single-output system dynamics. This segmentation allows operational issues to be detected and isolated by examining the dynamic response of each component separately, resolving the contradiction between integrated system functionality and detectability of operational issues.
2Measurement precision
If a nominal response model is generated using statistical bootstrap technique, then measurement precision of anomaly detection is improved, but device complexity increases
Solution Approach 1:
The statistical bootstrap technique enables the system to generate its own nominal response model by analyzing historical operational data without requiring external intervention or complex manual calibration. The system self-calibrates by statistically analyzing past performance to establish expected behavior patterns, thereby achieving high measurement precision for anomaly detection while avoiding the complexity of manual model setup and adjustment.
Data Source
AI summary
Methods and apparatus are provided for detecting an anomaly in fuel demand data and fuel supply data generated by a fuel metering system for an engine, The method comprises collecting the fuel demand data and the fuel supply data during operation of the fuel metering system, generating expected fuel supply data based on the collected fuel demand data and a nominal response model describing the expected behavior of the fuel metering system, and detecting the anomaly if a difference between the expected fuel supply data and the collected fuel supply data exceeds a predetermined threshold.