一个基于模拟的数据集,用于在混合运输网络中检测异常
Andrea Senese1, Saverio De Vito2, Elena Esposito2
1Department of Physics "Ettore Pancini", University of Naples Federico II, Via Cinthia,21 (Building 6), Naples 80126, Italy.
Data in brief
|February 18, 2026
概括
一个新的合成数据集解决了缺少用于监测气运输的数据的问题. 该资源有助于开发和测试用于更安全,更有效的基础设施的算法.
科学领域:
- 能源系统工程 能源系统工程
- 工业过程监控 工业过程监控
- 数据科学用于基础设施.
背景情况:
- 通过管道运输气对于可持续能源至关重要,但需要强有力的安全监测.
- 现有的运输网络的多变量数据集很少,这阻碍了数据驱动的监测方法的开发.
- 安全问题包括泄漏,压缩机故障和部件响应延迟的易燃性风险.
研究的目的:
- 为了解决运输网络多变量数据的有限可用性.
- 为开发和评估数据驱动的监控和异常检测算法提供合成数据集.
- 支持用于运输基础设施的数字双胞胎的发展.
主要方法:
- 使用MATLAB Simscape模拟一个代表性的工业气管道段.
- 从分布式虚拟传感器生成时间序列数据.
- 包括正常运行条件和各种异常场景 (泄漏,压缩机故障,反应延迟).
主要成果:
- 一个全面的合成数据集,捕捉运输的瞬态和稳态动态.
- 数据涵盖正常操作和关键故障场景.
- 该数据集适用于测试监控,异常检测和数字双胞胎的算法.
结论:
- 提出的合成数据集有效地填补了运输研究中的关键数据缺口.
- 该资源有助于开发和验证工业气管道的先进监测技术.
- 能够提高能基础设施的安全性,可靠性和效率.
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