数据集用于无人机问题识别和严重程度估计
Swardiantara Silalahi1, Tohari Ahmad1, Hudan Studiawan1
1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Data in brief
|February 9, 2026
概括
这项研究介绍了DroSev,这是一个新的数据集,用于识别无人机问题并估计其严重程度. 它可以通过日志消息分析更好地监测无人机的健康状况和预测性维护.
科学领域:
- 无人机技术 无人机技术
- 数据科学是数据科学.
- 机器学习 机器学习
背景情况:
- 无人机飞行日志包含操作安全的关键信息.
- 准确的问题识别和严重程度估计对于无人机维护至关重要.
研究的目的:
- 介绍DroSev,一个用于无人机问题识别和严重程度估计的新型数据集.
- 促进无人机自动健康监测和预测性维护方面的研究.
主要方法:
- 获取了Mendeley数据和AirData的无人机飞行日志消息.
- 开发了两个子任务:二进制问题识别和多类问题严重程度分类.
- 使用分层采样进行80:20列车测试分割.
主要成果:
- DroSev数据集为无人机日志分析提供了一个全面的资源.
- 该数据集支持无人机相关问题的识别和严重性评估.
- 总结了日志消息的语法特征.
结论:
- DroSev是推动无人机安全和可靠性的宝贵资源.
- 该数据集可用于训练机器学习模型,用于无人机自动诊断.
- 进一步的研究可以利用DroSev来改善无人机运营管理.
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