Cited by Blockage detection techniques for natural gas pipelines: A review

Liquid-filled pipeline leak detection and localization based on multi-scale residual networks DOI

Si-Liang Zhao,

Linhui Zhou, Shaogang Liu

et al.

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 35(5), P. 055012 - 055012

Published: Feb. 7, 2024

Abstract Effective ways to improve the accuracy of liquid-filled pipeline leak detection are one key issues that need be addressed urgently in a conservation-oriented society. Recently, methods based on deep learning have developed rapidly. To ability convolutional neural network for signal features and accuracy, multi-scale residual networks (MSRNs) model is proposed this paper localization. The uses kernels different scales extract multiscale leakage signals (DRNs) fully connected layers fuse features, thus improving Among them, large convolution kernel can acquire low-frequency information due its sizable perceptual field, medium capture local global signal, small more sensitive high-frequency signal. Meanwhile, test platform built evaluate model. results show localization MSRN 98.3%, which better than single-scale DRN In addition, verified good generalization noise immunity through testing analyzing under pressures background noises.

Language: Английский

Citations

Consequence analysis of a small-scale hydrogen leakage from the overhead hydrogen piping based on machine learning and physical modeling DOI

Yuki Suzuki,

Jo Nakayama, Tomoya Suzuki

et al.

Journal of Loss Prevention in the Process Industries, Journal Year: 2024, Volume and Issue: 90, P. 105328 - 105328

Published: May 7, 2024

Leakage from the overhead hydrogen piping (OHP) must be contained because of wide flammability range and low minimum ignition energy hydrogen; rapid leakage detection appropriate emergency responses are necessary to ensure safe OHP operation. Consequence analysis after is useful for hazardous-area estimation assists operators in emergency-response planning. However, consequence leakage-detection methods have not been combined so far, an integrated method has yet developed. This study aimed develop a based on machine learning (ML), physical modeling, analyses decision-making leakage. Initially, model was constructed using modeling; this that models target systems fundamental equations. Subsequently, ML various pressure or flow-rate profiles obtained model. Regression were estimate diameter. In addition, conducted effects explosions fires diameters predicted by regression We confirmed could distinguish between non-leakage conditions, estimated consequences used visualize risk level hazardous areas near points case studies. The results studies demonstrated effectiveness proposed decision making when leaks OHP. enables improved post-leakage situations.

Language: Английский

Citations

Leak detection in water supply pipeline with small-size leakage using deep learning networks DOI

Pengcheng Guo, Shumin Zheng, Jianguo Yan

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 191, P. 2712 - 2724

Published: Oct. 8, 2024

Language: Английский

Citations

Risk assessment and simulation of gas pipeline leakage based on Markov chain theory DOI

Yue Feng,

Jingqi Gao,

Xinwei Yin

et al.

Journal of Loss Prevention in the Process Industries, Journal Year: 2024, Volume and Issue: 91, P. 105370 - 105370

Published: June 12, 2024

Language: Английский

Citations

Efficient and Accurate Leakage Points Detection in Gas Pipeline using Reinforcement Learning-Based Optimization DOI

Qinglin He,

Lianjie Zhou, Feng Zhang

et al.

IEEE Sensors Journal, Journal Year: 2024, Volume and Issue: 24(17), P. 27640 - 27652

Published: July 16, 2024

Language: Английский

Citations

Gas pipeline leakage detection and localization method based on VMD-DTW DOI

Yang Wang, Wenzhuo Liu, Qiang Zhang

et al.

Flow Measurement and Instrumentation, Journal Year: 2025, Volume and Issue: unknown, P. 102820 - 102820

Published: Jan. 1, 2025

Language: Английский

Citations

An optimised variational mode decomposition method based on improved coati optimisation algorithm for blockage detection of natural gas pipelines DOI

Jianwu Chen,

Hao Xing,

Meixue Liu

et al.

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 39

Published: April 6, 2025

Language: Английский

Citations

Plastic Pipeline Leak Localization Based on Wavelet Packet Decomposition and Higher Order Cumulants DOI

Xiaojuan Han, Wanying Cao, Xiwang Cui

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2022, Volume and Issue: 71, P. 1 - 11

Published: Jan. 1, 2022

This paper presents a plastic pipeline leak localization method based on wavelet packet decomposition and higher order cumulants. The hydrophone sensors are used to capture the acoustic signals generated by water leakage in pipe. feature frequency band containing most information is subsequently extracted performing signals. suitable basis function for obtained comparing effects of varieties functions. Furthermore, cumulant estimate time delay between two signals, from which hole can be located. simulation experimental results show that, compared with basic cross-correlation (BCC) generalized (GCC), has accuracy. relative error third-order 2.99% experiment 1 7.58% 2, while fourth-order 1.96% 1.23% 2.

Language: Английский

Citations

Leak detection for natural gas gathering pipelines under corrupted data via assembling twin robust autoencoders DOI

Hao Zhang, Zhonglin Zuo, Zheng Li

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 188, P. 492 - 513

Published: May 27, 2024

Language: Английский

Citations

Adaptive signal extraction based on RLMD-SVD for water pipeline leakage localization DOI

Hongjin Liu, Hongyuan Fang, Xiang Yu

et al.

IEEE Sensors Journal, Journal Year: 2024, Volume and Issue: 24(16), P. 26522 - 26533

Published: Aug. 15, 2024

Language: Английский

Citations