Research on Valve Life Prediction Based on PCA-PSO-LSSVM

被引：4

作者：

Shi, Mingjiang ^{[1
]}

Tan, Peipei ^{[1
]}

Qin, Liansheng ^{[2
]}

Huang, Zhiqiang ^{[1
]}

机构：

[1] Southwest Petr Univ, Sch Mechatron Engn, Chengdu 610500, Peoples R China

[2] Southwest Petr Univ, Sch Elect & Informat Engn, Chengdu 610500, Peoples R China

来源：

PROCESSES | 2023年 / 11卷 / 05期

关键词：

ball valve; life prediction; principal component analysis; particle swarm optimization; least squares support vector machine; SUPPORT-VECTOR-REGRESSION; MACHINE; DIAGNOSIS; LOGS;

D O I：

10.3390/pr11051396

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The valve is a key control component in the oil and gas transportation system, which, due to the environment, transmission medium, and other factors, is susceptible to internal leakage, resulting in valve failure. Conventional testing methods cannot judge the service life of valves. Therefore, it is important to carry out valve life prediction research for oil and gas transmission safety. In this work, a valve service life prediction method based on the PCA-PSO-LSSVM algorithm is proposed. The main factors affecting valve service life are obtained by principal component analysis (PCA), the least squares support vector machine (LSSVM) is used to predict the valve service life, the parameters are optimized by using particle swarm optimization (PSO), and the valve service life prediction model is established. The results show that the predicted valve service life based on the PCA-PSO-LSSVM algorithm is closer to the actual value, with an average relative error (MRE) of 16.57% and a root mean square error (RMSE) of 1.2636. Valve life prediction accuracy is improved, which provides scientific and technical support for the maintenance and replacement of valves.

引用

页数：14

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