在小型漏洞泄漏条件下对天然气管道网络的检测方法的研究
Ying Zhao1, Lingxi Yang2,3, Qingqing Duan1
1Bocom Intelligent Information Technology Co., Ltd., Beijing 100102, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种新的时空注意网络 (STAN) 用于天然气管道泄漏检测,显著提高了小泄漏的准确性. 深度学习模型通过更好地识别管道问题来提高安全性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 天然气管道网络是关键的城市基础设施,因自然灾害和天气造成的泄漏而面临重大安全风险.
- 现有的泄漏检测方法难以有效地建模时间变化的管道结构数据,限制了它们的检测能力.
- 精确检测和定位天然气管道泄漏对于减轻危险至关重要.
研究的目的:
- 引入一种新的时空注意网络 (STAN),用于增强气管道泄漏检测,特别针对小孔泄漏条件.
- 改进在天然气管道中的动态空间和时间数据的建模,以更准确地识别泄漏.
- 减少泄漏风险,提高管道监控系统的整体性能.
主要方法:
- 利用图形注意网络 (GAT) 建模传感器之间的空间依赖关系,并捕获相邻节点的动态模式.
- 采用长期短期记忆 (LSTM) 模型,用于编码和解码时间序列数据的时间注意力机制.
- 在天然气管道模拟数据集上使用管道工作室软件评估拟议的STAN模型,并将其与最先进的方法进行比较.
主要成果:
- STAN模型实现了具有竞争力的性能指标:91.7%的精度,96.5%的回忆,F1得分为0.94.4.
- 在识别传感器状态和捕获泄漏检测至关重要的时间动态方面表现出有效性.
- 与现有方法相比,在检测小孔泄漏方面表现出更高的准确性.
结论:
- 拟议的时空注意网络 (STAN) 在天然气管道泄漏检测技术方面取得了重大进展.
- 深度学习技术,特别是时空建模,在克服当前泄漏检测系统的局限性方面非常有效.
- 开发的模型通过提高泄漏检测和定位的准确性和可靠性来提高管道安全性.
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