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<p>Manual testing of the mechanical parts of a satellite, such as reaction wheels, is often cost-inefficient, with multiple approaches for automating this process having been investigated. AI-driven testing tools can be employed towards this direction, reducing time and effort, although human intervention must be still kept in the loop, to ensure test robustness and coverage completability. This paper employs machine learning models, with the aim to automate the test generation process for a reaction wheel, in the context of a passive rundown test scenario. Towards this direction, a relevant simulator is utilized, specifying different models for different wheel types. A Support Vector Machine with RBF Kernel is trained guided by an efficient Active Learning technique, in order to generate adequate passive rundown test cases for three reaction wheel profiles (a heavy, a medium and a light wheel). The model is supported by an automated test evaluation script that 1 Final revised PDF analyses simulation speed outputs, labeling each test case as “adequate” or “not adequate”. The proposed approach is evaluated against a big dataset of labeled test cases for all wheel types. The experimental evaluation illustrated a high precision (1.0, 0.85 and 0.70 for the three reaction wheel profiles respectively) achieved by the SVM-RBF approach, in contrast to other competing methods (generative or classification). Lastly, as a practical experiment, four generated test cases are ran in a lab reaction wheel, estimating its friction parameters, in order to assess its health status. Calculating the observed friction values based on the time to stop parameter, the reaction wheel is finally characterized as healthy.</p>

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An analytical comparison of machine learning- based techniques for test case generation across diverse reaction wheel systems

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Fotis Aisopos,
  • Dimitrios Vogiatzis,
  • Mário Pinto,
  • Paschalis Veskos,
  • Georgios Paliouras,
  • Robert Blommestijn

摘要

Manual testing of the mechanical parts of a satellite, such as reaction wheels, is often cost-inefficient, with multiple approaches for automating this process having been investigated. AI-driven testing tools can be employed towards this direction, reducing time and effort, although human intervention must be still kept in the loop, to ensure test robustness and coverage completability. This paper employs machine learning models, with the aim to automate the test generation process for a reaction wheel, in the context of a passive rundown test scenario. Towards this direction, a relevant simulator is utilized, specifying different models for different wheel types. A Support Vector Machine with RBF Kernel is trained guided by an efficient Active Learning technique, in order to generate adequate passive rundown test cases for three reaction wheel profiles (a heavy, a medium and a light wheel). The model is supported by an automated test evaluation script that 1 Final revised PDF analyses simulation speed outputs, labeling each test case as “adequate” or “not adequate”. The proposed approach is evaluated against a big dataset of labeled test cases for all wheel types. The experimental evaluation illustrated a high precision (1.0, 0.85 and 0.70 for the three reaction wheel profiles respectively) achieved by the SVM-RBF approach, in contrast to other competing methods (generative or classification). Lastly, as a practical experiment, four generated test cases are ran in a lab reaction wheel, estimating its friction parameters, in order to assess its health status. Calculating the observed friction values based on the time to stop parameter, the reaction wheel is finally characterized as healthy.