Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM

CHEN Yong-hong, SU Yong-sheng, HU Ping

doi:10.16577/j.cnki.42-1215/tb.2021.01.010

Materials Protection >

2021 , Vol. 54 >Issue 1: 63 - 67

DOI: https://doi.org/10.16577/j.cnki.42-1215/tb.2021.01.010

Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM

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(1.College of Mechanical and Electrical Engineering, Wuhan Donghu University, Wuhan 430212, China;2a.College of Power Engineering, 2b.College of Ships and Oceangraphy, Naval Univeristy of Engineering, Wuhan 430030, China)

Received date: 2020-07-11

Online published: 2025-07-09

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Abstract

Corrosion rate is an important characteristic parameter to reflect the corrosion dynamics process of pipeline.In order to accurately evaluate the long-term operation reliability and remaining life of pipeline, the prediction of corrosion rate is particularly important.Least squares support vector machine(LSSVM)is a method based on machine learning, which is often used in classification and prediction research.Since penalty parameters γ and kernel parameters σ2 are two important parameters of LSSVM, the value of these two parameters can only be obtained by experience in calculation, causing a great impact on the calculation results.In this paper, the genetic algorithm(GA)was used to optimize the parameters, the GA-LSSVM prediction model was built and the model was applied to the prediction of pipeline corrosion rate.Compared with the results of other prediction models, the results showed that the accuracy of GA-LSSVM model and prediction results were relatively higher, which could realize the prediction of pipeline corrosion rate.

Key words： least squares support vector machine(LSSVM); genetic algorithm(GA); corrosion rate; prediction

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CHEN Yong-hong, SU Yong-sheng, HU Ping . Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM[J]. Materials Protection, 2021 , 54(1) : 63 -67 . DOI: 10.16577/j.cnki.42-1215/tb.2021.01.010

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