A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification-Reference-Cited by-同舟云学术

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A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification

Published:2021-11 Issue: Volume:182 Page:108255
ISSN:0003-682X
Container-title:Applied Acoustics
language:en
Short-container-title:Applied Acoustics

Author:

Ning Fangli,Cheng Zhanghong,Meng Di,Wei Juan

Funder

National Natural Science Foundation of China

Chinese Aeronautical Establishment Aeronautical Science Foundation

Shaanxi Province Key Research and Development Projects

Publisher

Elsevier BV

Subject

Acoustics and Ultrasonics

Reference32 articles.

1. Received signal strength difference based leakage localization for the underwater natural gas pipelines;Mahmutoglu;Appl Acoust,2019

2. Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference;Zhang;Reliab Eng Syst Saf,2020

3. Modeling dynamic pressure of gas pipeline with single and double leakage;Ndalila;IEEE Sens J,2021

4. Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (mlpnn);Zadkarami;J Loss Prevent Process Ind,2016

5. Wu X, Lu H, Huang K, Yuan Z, Sun X. Mathematical model of leakage during pressure tests of oil and gas pipelines. J Pipeline Syst Eng Pract 6(4).

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