Aircraft Test Matrix Evaluation for Cross-System Coverage Gaps
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Solution Overview
Problem
Operational testing of aircraft systems is costly and time-consuming, and existing methods often fail to detect unexpected effects of changes on other systems, as tests are typically performed individually and sequentially.
Innovation Solution
A method that involves obtaining and processing sensor data from multiple aircraft systems using analytic models to evaluate operational tests, generating predicted sensor data, and calculating error measures and test coverage metrics to identify any unexpected effects across systems during a single test session.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If operational tests are performed individually for each aircraft system, then the testing process is simple to manage and organize, but the total testing time and cost increase significantly
Solution Approach 1:
The patent combines multiple individual system tests into a single integrated operational test. The flight test system simultaneously collects sensor data from multiple aircraft systems (engine, fuel, electrical, etc.) during one flight session, eliminating the need to perform separate tests for each system. This merging approach reduces total testing time while maintaining comprehensive coverage of all systems through unified data collection and analysis.
2Measurement precision
If individual system tests are performed, then each system can be tested thoroughly, but unexpected effects on other systems remain undetected
Solution Approach 1:
The flight test system performs multiple functions simultaneously: it tests each individual aircraft system while also detecting interactions and unexpected effects between systems. The sensor network and analytic models are designed to evaluate both isolated system performance and system-to-system influences, providing comprehensive validation in a single test session. This multi-functional approach maintains precise measurement of individual systems while simultaneously improving detection of cross-system effects.
3Manufacturing precision
If a large number of separate tests are conducted, then complete test coverage is achieved, but the testing process becomes costly and time-consuming
Solution Approach 1:
The system enables continuous data collection and evaluation across multiple systems during a single uninterrupted flight test. Instead of stopping between tests for different systems, the sensor network continuously monitors all aircraft systems simultaneously, and the analytic models continuously evaluate the collected data. This continuous operation maintains complete test coverage while dramatically improving testing efficiency by eliminating idle time between separate test sequences.
Data Source
AI summary
A method includes obtaining a first test matrix for a first aircraft system and a second test matrix for a second aircraft system. The method also includes, during a first operational test of the first test matrix, obtaining sensor data that includes second sensor data that is not specified by the first test matrix. The method includes evaluating a second operational test of the second test matrix by processing the second sensor data using a second analytic model of the second aircraft system. The method also includes generating second predicted sensor data based on the evaluation of the second operational test. The method includes generating a second error measure by comparing a second subset of the sensor data to the second predicted sensor data. The method includes determining, based at least in part on a range of the second sensor data, a test coverage metric of the second test matrix.