Aircraft Control Module Testing for Neural Network Corruption
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Solution Overview
Problem
Aircraft control systems employing machine learning models, particularly neural networks, are susceptible to corruption from electromagnetic radiation, leading to potential erroneous control outputs, which can compromise aircraft safety.
Innovation Solution
An aircraft control system with a test module that compares actual control outputs from a neural network with expected outputs, and upon detection of discrepancies, selectively removes the authority of the neural network by powering it off, and switches to a backup system, such as a manual or secondary neural network, to ensure reliable control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If machine learning models are employed in aircraft control systems, then development time is decreased, but reliability deteriorates due to susceptibility to corruption from electromagnetic radiation
Solution Approach 1:
The patent implements preliminary testing of machine learning models before deployment in aircraft control systems. The test module executes predefined test cases with known inputs and expected outputs to verify model integrity and detect corruption early, preventing unreliable models from being deployed to critical flight control functions
Solution Approach 2:
The patent establishes a feedback mechanism where the test module continuously monitors the machine learning model's outputs during operation. When discrepancies between actual and expected outputs are detected, the system provides feedback to disable or reset the model, ensuring that corruption is detected and addressed to maintain reliability
2Productivity
If machine learning models are used for aircraft control, then productivity is improved, but safety deteriorates due to potential erroneous control outputs
Solution Approach 1:
The patent introduces a test module as an intermediary between the machine learning model and the aircraft control system. This intermediary layer validates the model's outputs before they are applied to control aircraft functions, blocking erroneous outputs while allowing valid productivity-enhancing control actions to proceed
Solution Approach 2:
The patent implements protective measures beforehand by storing expected outputs for test cases and preparing validation logic in advance. This cushioning mechanism ensures that when the machine learning model operates, its outputs are immediately validated against predetermined expectations, preventing unsafe control actions before they can affect aircraft operations
3Reliability
If testing is performed on aircraft control module, then reliability is improved, but loss of time increases due to testing duration
Solution Approach 1:
The patent implements partial testing by executing a selected subset of test cases rather than running all possible tests. The test module chooses representative test cases that cover critical failure modes and validation scenarios, achieving sufficient reliability verification without the excessive time cost of exhaustive testing of all possible inputs
Data Source
AI summary
An aircraft control system (100) including an aircraft control module (110) and a test module (120). The aircraft control module includes a memory storing a machine learning model (130) adapted, based on received live aircraft operating inputs (102a-102f), to generate live aircraft control outputs (104a-104b). The test module (120) includes a memory storing input data representing test aircraft operating inputs (102a′-102f′) and respective output data representing expected aircraft control outputs (114a, 114b) that are expected to be generated by the aircraft control module in response to an input of the respective test aircraft operating inputs (102a′-102f′). The test module (120) is adapted to write test aircraft operating inputs (102a′-102f′) to the aircraft control module, read test aircraft control outputs (104a′-104b′) of the aircraft control module (110), and remove the authority of the aircraft control module (110) to perform aircraft control.