Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

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1.60

CiteScore

Jingzong Yang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Tianqing Yang This email address is being protected from spambots. You need JavaScript enabled to view it.1, and Chunchao Shi1

1School of Big Data, Baoshan University, BaoShan, Yunnan, 678000, P.R.China


 

Received: October 18, 2021
Accepted: November 8, 2021
Publication Date: December 6, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202208_25(4).0018  


ABSTRACT


Aiming at the difficulty of pipeline blockage fault identification, a fault identification method based on multiresolution permutation entropy and artificial bee colony (ABC) algorithm optimized support vector machine (SVM) is proposed. Firstly, the acoustic active detection method is used to collect the signals under different blocking conditions. Then, the extracted acoustic impulse response signal is decomposed into different scale components by using wavelet transform algorithm, and the arrangement entropy of each component is extracted based on the arrangement entropy theory. And then, based on the permutation entropy theory, the permutation entropy in each component is extracted to effectively characterize the different levels of nonlinear features in the signal. Secondly, the artificial bee colony algorithm is used to optimize the penalty factor and kernel function of support vector machine, and the optimized parameters are used to construct the fault identification model. The results show that the proposed method can improve the identification accuracy of pipeline blockage fault. Meanwhile, compared with the traditional SVM and feed forward neural network with back propagation (BP) model, it has better effect.


Keywords: Multiresolution analysis; Permutation entropy; Support vector machine; Pipeline; Blockage; Fault identification


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