公开(公告)号：US20150308919A1

公开(公告)日：2015-10-29

申请号：US14692502

申请日：2015-04-21

Applicant: Northeastern University

Inventor： Huaguang ZHANG ,  Dazhong MA ,  Jian FENG ,  Jinhai LIU ,  Gang WANG ,  Zhenning WU ,  Qiuye SUN ,  Xiaoyu LI

IPC: G01M3/28

CPC classification number: G01M3/2807 ,  G01M3/243

Abstract: The present invention relates to an intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data. The present invention effectively analyzes a large amount of data collected on site within a reasonable time period and obtains a state of a pipeline network by an intelligent adaptive method, thereby obtaining a topological structure of a pipeline network. The present invention specifically adopts a flow balance method in combination with information conformance theory to analyze whether the pipeline network has leakage; small amount of leakage and slow leakage can be perfectly and accurately alarmed upon detection; as a generalized regression neural network is adopted to locate a leakage of the pipeline network, an accuracy of a result is increased. Therefore, the present invention adopts a policy and intelligent adaptive method based on big data to solve problems of detecting and locating leakage of the pipeline network.

Abstract translation: 本发明涉及一种基于大数据监测油管网泄漏的智能自适应系统和方法。 本发明有效地分析了在合理时间段内现场收集的大量数据，并通过智能自适应方法获得了管网的状态，从而获得了管网的拓扑结构。 本发明具体采用流量平衡法与信息一致性理论相结合，分析管网是否有泄漏; 检测时可以完全准确地报警泄漏量低，泄漏量低; 采用广义回归神经网络来定位管网泄漏，提高了精度。 因此，本发明采用基于大数据的策略和智能自适应方法来解决检测和定位管网泄漏的问题。

公开(公告)号：US20230341354A1

公开(公告)日：2023-10-26

申请号：US18028010

申请日：2020-11-06

Applicant: Northeastern University

Inventor： Huaguang ZHANG ,  Jinhai LIU ,  Lei WANG ,  Jiayue SUN ,  Jian FENG ,  Gang WANG ,  Dazhong MA ,  Senxiang LU

IPC: G01N27/83 ,  G01B7/02

CPC classification number: G01N27/83 ,  G01B7/02

Abstract: Provided is an intelligent inversion method for pipeline defects based on heterogeneous field signals. The method includes the following steps: firstly, acquiring heterogeneous field signals, performing an abnormality judgement, then correcting base values of the heterogeneous field signals, and performing denoising treatment; padding the denoised heterogeneous field signals corresponding to the pipeline defects, unifying the heterogeneous field signals of different sizes into the heterogeneous field signals of same sizes, and performing a nonlinear transformation on signal amplitudes; designing a sparse autoencoder with an axisymmetric structure, and obtaining primary characteristics of the heterogeneous field signals; classifying the pipeline defects according to lengths, widths and depths to obtain category labels of the pipeline defects; designing a multi-classification neural network to classify the heterogeneous field signals, and extracting deep characteristics containing defect size information; and constructing a random forest regression model to realize intelligent inversion for sizes of the pipeline defects.