在水分网络中使用集成生成模型进行先进的声学泄漏检测
Rongsheng Liu1, Tarek Zayed1, Rui Xiao1
1Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
Water research
|March 14, 2024
概括
本研究介绍了一种LSTM-GAN方法,用于生成合成声学泄漏信号,克服数据稀缺性,以改进水分网的水泄漏检测. 这种方法提高了机器学习模型的稳定性和准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 水分网络 (WDNs) 由于泄漏而遭受大量水损失,因此需要有效的检测方法.
- 当前的机器学习 (ML) 声学泄漏检测受到有限和非多样化的数据集的阻碍.
研究的目的:
- 提出和评估一种LSTM-GAN方法来生成合成声漏信号.
- 通过增强有限的数据集来增强基于ML的水泄漏检测在WDN中.
主要方法:
- 从WDN收集声信号来训练长期短期记忆生成对抗网络 (LSTM-GAN) 模型.
- 使用LSTM-GAN生成合成泄漏信号来增强原始数据集.
- 使用t-SNE和声学特征评估生成方法有效性;使用原始和增强数据集比较LSTM泄漏检测模型.
主要成果:
- 该LSTM-GAN模型成功地产生了高质量的,一致的合成声学信号,表明存在泄漏.
- 在增强数据集上训练的泄漏检测模型与仅使用原始数据的模型相比,显示出更好的性能.
- 拟议的LSTM-GAN方法在提高泄漏检测方面优于其他声信号生成技术.
结论:
- LSTM-GAN方法有效地解决了基于ML的WDN声学泄漏检测中的数据稀缺问题.
- 生成的合成数据显著提高了水泄漏检测模型的稳定性和性能.
- 这种生成方法为基础设施监控中的数据有限机器学习应用提供了创新的解决方案.
更多相关视频
12:26Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics
Published on: August 27, 2013
17.1K
07:14Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
Published on: May 1, 2018
7.8K
相关概念视频
01:30Typical Model Studies
359
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
359
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
165
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
165
01:24Rapidly Varying Flow
61
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
61
01:25Design Example: Designing a Residential Plumbing System
684
The design of residential plumbing systems requires carefully evaluating water demand, flow rates, and pressure dynamics to ensure both efficiency and reliability. The nature of water flow within pipes is defined by its Reynolds number, which classifies flow as either laminar (smooth) or turbulent.
684