|本期目录/Table of Contents|

[1]张瑞程,王新颖,胡磊磊,等.基于一维卷积神经网络的燃气管道泄漏声发射信号识别*[J].中国安全生产科学技术,2021,17(2):104-109.[doi:10.11731/j.issn.1673-193x.2021.02.016]
　ZHANG Ruicheng,WANG Xinying,HU Leilei,et al.Acoustic emission signal identification of gas pipeline leakage based on one-dimensional convolution neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(2):104-109.[doi:10.11731/j.issn.1673-193x.2021.02.016]
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基于一维卷积神经网络的燃气管道泄漏声发射信号识别*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
17
期数:
2021年2期
页码:
104-109
栏目:
职业安全卫生管理与技术
出版日期:
2021-02-28

文章信息/Info

Title:
Acoustic emission signal identification of gas pipeline leakage based on one-dimensional convolution neural network
文章编号:
1673-193X(2021)-02-0104-06
作者:
张瑞程; 王新颖; 胡磊磊; 林振源; 黄旭安; 赵斌
（常州大学 环境与安全工程学院，江苏 常州 213164）
Author(s):
ZHANG Ruicheng;  WANG Xinying;  HU Leilei;  LIN Zhenyuan;  HUANG Xuan;  ZHAO Bin
(School of Environment and Safety Engineering,Changzhou University,Changzhou Jiangsu 213164,China)
关键词:
故障诊断; 一维卷积; 燃气管道; 声发射; 混淆矩阵
Keywords:
fault diagnosis;  one-dimensional convolution;  gas pipeline;  acoustic emission;  confusion matrix
分类号:
X933.4
DOI:
10.11731/j.issn.1673-193x.2021.02.016
文献标志码:
A
摘要:
为保障燃气管道系统安全运行，及时诊断管道故障，基于VGG-16模型提出基于一维卷积神经网络的燃气管道故障诊断模型，提取原始声发射信号特征参数，有效诊断燃气管道故障。结果表明：基于一维卷积神经网络的燃气管道故障诊断模型，能够有效解决燃气管道故障诊断过程中数据预处理复杂、特征提取困难以及识别准确率低等问题，可为燃气管道故障诊断提供技术支撑。
Abstract:
In order to carry out the fault diagnosis of urban gas pipeline timely and accurately,thus ensure the safe operation of gas pipeline system,aiming at the problems of fault diagnosis in the traditional fault diagnosis methods such as the data preprocessing was complex and it was difficult to solve the endtoend,a fault diagnosis algorithm of gas pipeline based on the one-dimensional convolution neural network was proposed on the basis of the classical model VGG-16.This method could directly extract and select the features of the original acoustic emission signals without any transformation in advance.By building a one-dimensional convolution model on Keras,the acoustic emission signals of gas pipelines were collected in the laboratory,and various model evaluation methods such as accuracy,confusion matrix and recall rate were used to evaluate the fault diagnosis effect of the model.The results showed that the fault diagnosis model of gas pipeline based on the one-dimensional convolution neural network can effectively solve the problems of complex data preprocessing,difficult feature extraction and low recognition accuracy in the fault diagnosis process of gas pipelines.

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备注/Memo

备注/Memo:
收稿日期： 2020-07-30
* 基金项目： 江苏省研究生科研与实践创新计划项目（KYCX20_2590）
作者简介： 张瑞程，硕士研究生，主要研究方向为安全检测技术。
通信作者： 王新颖，硕士，副教授，主要研究方向为安全检测技术。
更新日期/Last Update: 2021-03-11