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[1]刘恩斌,温櫂荣,郭冰燕,等.基于声信号特征分析的燃气管道探测识别方法*[J].中国安全生产科学技术,2022,18(4):61-68.[doi:10.11731/j.issn.1673-193x.2022.04.009]
　LIU Enbin,WEN Zhaorong,GUO Bingyan,et al.Detection and recognition methods of gas pipelines based on acoustic signal feature analysis[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(4):61-68.[doi:10.11731/j.issn.1673-193x.2022.04.009]
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基于声信号特征分析的燃气管道探测识别方法*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年4期
页码:
61-68
栏目:
职业安全卫生管理与技术
出版日期:
2022-04-30

文章信息/Info

Title:
Detection and recognition methods of gas pipelines based on acoustic signal feature analysis
文章编号:
1673-193X(2022)-04-0061-08
作者:
刘恩斌; 温櫂荣; 郭冰燕; 喻斌; 陈其琨
（1.西南石油大学 石油与天然气工程学院，四川 成都 610500；
2.中国石油化工股份有限公司 天然气分公司，北京 100029；
3.中石油华北油田分公司，河北 任丘 062550；
4.中国石油管道局工程有限公司，河北 廊坊 065000；
5.School of Engineering,Cardiff University，UK Cardiff CF24 3AA）
Author(s):
LIU Enbin;  WEN Zhaorong;  GUO Bingyan;  YU Bin;  CHEN Qikun
(1.Petroleum Engineering School,Southwest Petroleum University,Chengdu Sichuan 610500,China;
2.SINOPEC Gas Company,Beijing 100029,China;
3.CNPC Huabei Oil Field Branch,Renqiu Hebei 062550,China;
4.China Petroleum Pipeline Engineering Corporation,Langfang Hebei 065000,China;
5.School of Engineering,Cardiff University,Cardiff CF24 3AA,UK)
关键词:
燃气管道; 声信号; 希尔伯特黄变换; 特征提取; BP神经网络; 模式识别
Keywords:
gas pipeline;  acoustic signal;  Hilbert-Huang transform;  feature extraction;  BP neural network;  pattern recognition
分类号:
X937
DOI:
10.11731/j.issn.1673-193x.2022.04.009
文献标志码:
A
摘要:
为了探测和辨识地下燃气管道，避免燃气管道改扩建的过程发生第三方破坏引发安全事故，提出1种基于声信号特征分析的燃气管道探测识别方法，该方法考虑燃气管道声信号声压级低以及易衰减的特点，采用Hilebert-Huang变换算法分析燃气管道流噪声信号特征，建立燃气管道流噪声信号的特征数据库，并通过BP神经网络进行模式识别，判别管道的种类以及在役状态。通过对实测数据和数值模拟数据的分析表明：该方法的有效识别率达到了97.5％，验证了该方法的有效性。
Abstract:
In order to detect and identify underground gas pipeline and to avoid safety accidents caused by third-party damage during the reconstruction and expansion of gas pipeline,a gas pipeline detection and identification method based on acoustic signal feature analysis was proposed，considering the characteristics of low sound pressure level and easy attenuation of gas pipeline acoustic signal.The Hilbert Huang transform algorithm was used to analyze the characteristics of gas pipeline flow noise signal,and the characteristic database of gas pipeline flow noise signal was established,BP neural network was used for pattern recognition to distinguish the type of pipeline and in-service state.The analysis of measured data and numerical simulation data showed that the effective recognition rate of this method reaches 97.5%,which verifies the effectiveness of this method.

参考文献/References:

［1］LIU E B,WANG X J,ZHAO W W,et al.Analysis and research on pipeline vibration of a natural gas compressor station and vibration reduction measures［J］.Energy & Fuels,2021,35(1):479-492.
［2］PENG Y,LIU E B,PENG S B,et al.Using artificial intelligence technology to fight COVID-19:A review ［J］.Artificial Intelligence Review,2022,55(1):1-37.
［3］LIU E B,WEN Z R,GUO B Y,et al.Research on detection and recognition methods of gas pipelines based on acoustic signal feature analysis ［J］.Journal Of Vibration And Control,2022.DOI:10.1177/10775463221082754.
［4］PHANIKRISHNA B V,CHINARA S.Automaticclassification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal［J］.Journal of Neuroscience Methods,2021,347:108927.
［5］LIU E B,KOU B,WU P N,et al.Performance analysis and structure optimization of shale gas desander ［J］.Energy Sources,2022.DOI:10.1080/15567036.2022.2040656.
［6］LIU E B,LIAN D P,ZHENG H,et al.Research on Abnormal Vibration and Vibration Reduction Measures of a Natural Gas Compressor Station:A Case Study of the JYG Compressor Station［J］.Energy & Fuels,2022,36(2):897-909.
［7］LIU E B,LI D J,LI W S,et al.Erosion simulation and improvement scheme of separator blowdown system：A case study of Changning national shale gas demonstration area［J］.Journal of Natural Gas Science and Engineering,2021,88:103856.
［8］SHI J,WANG L H,LEE W J,et al.Hybridenergy storage system (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction［J］.Applied Energy,2019,256:113915.
［9］WEI H C,XIAO M X,CHEN H Y,et al.Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects［J］.Scientific Reports,2018,8(1):15771.
［10］ZOLTAN G S,HORATIU S G.Hilbert-Huangtransform in fault detection［J］.Procedia Manufacturing,2019,32:591-595.
［11］ZHAO G Q,ZHANG L,WANG B,et al.HHT-based AE characteristics of 3D braiding composite shafts［J］.Polymer Testing,2019,79:106019.
［12］FARIDYOUSEFI H,SALEM M K,GHORANNEVISS M.MHD mode identification from mirnov coils signals in tokamak via combination of singular value decomposition and Hilbert-Huang transform analysis methods［J］.Journal Of Fusion Energy,2020,39(6):512-520.
［13］SOUFIEN E,ESSAIEB H.Ultrasonic rock microcracking characterization and classification using Hilbert-Huang transform［J］.Innovative Infrastructure Solutions,2020,5(3):100.
［14］ONDRA V,SEVER I A,SCHWINGSHACKL C W.Identification of complex non-linear modes of mechanical systems using the Hilbert-Huang transform from free decay responses［J］.Journal Of Sound And Vibration,2021,495:115912.
［15］ZHANG J,TOUNZI A,BENABOU A,et al.Detection of magnetization loss in a PMSM with Hilbert Huang transform applied to non-invasive search coil voltage［J］.Mathematics And Computers In Simulation,2021,184:184-195.
［16］张雅晶,董文彬,杨拓宇,等.基于BP神经网络的激光弯曲成形工艺参数优化［J］.塑性工程学报,2020,27(8):66-71. ZHANG Yajing，DONGWenbin，YANG Tuoyu，et al.Process parameters optimization of laser bending based on BPneural network\[J\].Journal of Plasticity Engineering,2020,27(8):66-71.

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备注/Memo

备注/Memo:
收稿日期： 2021-08-18
* 基金项目： 中石油重大科技项目（2021DJ2804）；四川省应用基础项目（2019YJ0352）
作者简介： 刘恩斌，博士，教授，主要研究方向为油气管网仿真与优化技术、瞬变流、计算流体力学。
通信作者： 温櫂荣，硕士研究生，主要研究方向为油气储运系统仿真与优化。
更新日期/Last Update: 2022-05-13