Aircraft Classifier Testing With Scenario-Based Error Categorization
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Solution Overview
Problem
Developing aircraft control systems with trained classifiers is complex and time-consuming, and identifying errors in their unexpected behavior is difficult due to lack of transparency, necessitating improved methods for evaluating and categorizing errors in these systems.
Innovation Solution
A test apparatus and method using scenario data and a graph model to evaluate trained classifiers, generating error categorization data that identifies and categorizes errors in aircraft systems, allowing for efficient troubleshooting and rapid development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If trained classifiers are used to control aircraft systems, then development time may be decreased, but it becomes difficult to determine the cause of unexpected behavior and ensure sufficient trust in the system
Solution Approach 1:
The patent segments the error analysis process by categorizing errors into distinct types (configuration errors, data errors, classifier errors, etc.). This segmentation allows developers to identify and address specific error sources in trained classifiers, making the previously opaque error detection process structured and manageable while maintaining the productivity benefits of using trained classifiers.
Solution Approach 2:
The patent introduces an intermediary testing system that acts as a mediator between the trained classifier and the aircraft system. This intermediary captures and analyzes classifier outputs, generating error categorization data that reveals the internal state and decision-making process of the trained classifier, thereby enabling error detection without requiring modification of the classifier itself.
2Reliability
If traditional error detection methods are used for trained classifiers, then system reliability may be maintained, but troubleshooting and development efficiency are significantly reduced
Solution Approach 1:
The patent implements preliminary error categorization by analyzing classifier outputs during testing phases before deployment. Error categorization data is generated in advance, identifying potential issues with configuration, input data, or classifier behavior. This preliminary analysis establishes a foundation for rapid troubleshooting while maintaining system reliability through thorough pre-deployment validation.
Solution Approach 2:
The patent implements feedback mechanisms where error categorization results are fed back to developers to guide troubleshooting efforts. The system continuously monitors classifier performance, categorizes errors systematically, and provides actionable feedback that reduces troubleshooting time while maintaining reliability through iterative improvement of the trained classifier based on identified errors.
3Ease of repair
If detailed error analysis is implemented for trained classifiers, then troubleshooting efficiency is improved, but system complexity and testing requirements increase
Solution Approach 1:
The patent extracts the complex error analysis functionality from the core aircraft control system into a separate testing and evaluation module. This extraction allows detailed error categorization and analysis to be performed independently, improving troubleshooting efficiency for the trained classifier without adding complexity to the critical aircraft control system itself. The testing system handles the analytical complexity while the control system maintains its simplicity and reliability.
Data Source
AI summary
A test apparatus and method for testing a trained classifier configured to control an aircraft system. Scenario data includes operating inputs representing an operational state of an aircraft and classifier outputs for controlling the aircraft system, is obtained. Model data representing a model of the at aircraft system is obtained. Error categorisation data for each aircraft operating scenario is generated based on respective classifier outputs applied to the model.