Aircraft Neural Network Corruption Detection With Test-Input Validation
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Solution Overview
Problem
Aircraft control systems employing machine learning models, such as neural networks, are susceptible to corruption due to electromagnetic radiation, leading to potential erroneous aircraft control outputs.
Innovation Solution
An aircraft control system comprising a test module that monitors the operating status of the aircraft control module, writes test inputs, reads outputs, and selectively removes authority if outputs do not conform to expected values, with a backup system to replace the aircraft control module in case of corruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If machine learning models are used in aircraft control systems, then development time is reduced, 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 performs corruption tests by introducing electromagnetic radiation interference during the testing phase, allowing the system to identify and correct vulnerabilities before actual operation, thus maintaining both reduced development time and improved reliability
Solution Approach 2:
The patent establishes a feedback mechanism where the test module continuously monitors the machine learning model's performance and compares it against expected outcomes. When corruption is detected through feedback from test results, the system can alert operators or automatically switch to backup models, maintaining reliability while preserving the efficiency benefits of machine learning
2Extent of automation
If machine learning models are deployed in aircraft control, then automation is increased, but safety deteriorates due to potential corruption leading to 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 continuously validates the model's outputs by comparing them against expected results and can intercept erroneous control signals caused by corruption, thereby maintaining high automation while ensuring safety through an intermediate validation layer
Solution Approach 2:
The patent prepares backup machine learning models in advance that can immediately replace corrupted primary models. This beforehand cushioning ensures that if corruption occurs during automated operation, the system can switch to a pre-prepared backup model without interrupting flight control, thus maintaining both automation and safety
Data Source
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AI summary
Disclosed is an aircraft control system (100) comprising an aircraft control module (110) and a test module (120). The aircraft control module (110) comprises 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), to perform aircraft control. The test module (120) comprises 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, while the aircraft control module (110) is not performing aircraft control, 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) generated based on the respective test aircraft operating inputs (102a'-102f'), and selectively remove the authority of the aircraft control module (110) to perform aircraft control if the test aircraft control outputs (104a'-104b') do not conform to the respective expected aircraft control outputs (114a-114b).