一个概率数字双胞胎,用于在供水网络中使用生成式深度学习来定位泄漏
Nikolaj T Mücke1,2, Prerna Pandey3, Shashi Jain3
1Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了一种新的深度学习和贝叶斯推理方法,用于确定水系统中的泄漏. 该方法提供快速,准确和可靠的泄漏本地化与不确定性量化.
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:
- 由于网络的复杂性,有限的传感器和杂的数据,在水分配系统中泄漏局部化是复杂的.
- 精确的泄漏检测对于高效的水资源管理和基础设施维护至关重要.
研究的目的:
- 开发一套强大的方法,用于在大型供水系统中的泄漏局部化.
- 将不确定性量化纳入泄漏局部化过程中.
- 利用生成式深度学习和贝叶斯推理来提高准确性和速度.
主要方法:
- 使用神经网络的生成深度学习模型作为复杂系统方程的概率替代品.
- 贝叶斯推理用于将传感器数据与替代模型的输出结合起来.
- 该方法量化了泄漏位置预测中的不确定性.
主要成果:
- 该方法证明了快速,准确和可靠的泄漏本地化在三个越来越复杂的测试案例.
- 平均拓距离 (ATD) 从0.3到10不等,取决于网络的复杂性和噪声水平.
- 对各自的测试案例实现了83%,72%和42%的准确性,计算时间从0.1到13秒.
结论:
- 提出的生成深度学习和贝叶斯推理方法为水网络中泄漏局部化提供了强大的工具.
- 这种方法提供了一个可靠的数字双胞胎解决方案,集成先进的数学和深度学习技术.
- 该方法有效地解决了水分系统中不确定性和复杂性的挑战.
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