天然气与石油2024，Vol.42Issue(4)：94-100,7.DOI:10.3969/j.issn.1006-5539.2024.04.012
基于改进核函数的支持向量机天然气脱硫装置故障诊断方法
Fault diagnosis using Support Vector Machines(SVM)with improved kernel functions in natural gas desulfurization system
摘要
Abstract
To address the issue of slow response and low diagnostic accuracy inherent in traditional desulfurization system fault diagnosis methods,this paper proposes a Support Vector Machine(SVM)fault diagnosis method for natural gas desulfurization system based on improved kernel function.According to Mercer's theory,this method redesigns the SVM's kernel function and its parameters,by integrating polynomial,Sigmoid,and radial basis kernel functions into a single composite kernel.Compared with the traditional SVM method,this improved approach not only combines the benefits of each single kernel function but also offers higher learning efficiency and diagnostic accuracy,maintaining strong generalization capabilities even with limited sample data.Experimental validation conducted using HYSYS modeling and field data demonstrates that the improved method reduces error rate to approximately 30%of the its original method before improvement,thereby verifying the method's effectiveness in improving the accuracy and efficiency of the fault diagnosis of desulfurization systems.The research results contribute to the intelligent operation of fault diagnosis systems in natural gas desulfurization systems and also provide a reference for the study of fault diagnosis methods.关键词
改进核函数/支持向量机/HYSYS/天然气脱硫/故障诊断Key words
Improved kernel functions/Support Vector Machines(SVM)/HYSYS/Natural gas desulfurization/Fault diagnosis引用本文复制引用
何宇琪,张波,王俊超,熊鹏..基于改进核函数的支持向量机天然气脱硫装置故障诊断方法[J].天然气与石油,2024,42(4):94-100,7.基金项目
科技部重点研发计划"大型旋转机组健康管理系统软件"(2020YFB1709800) （2020YFB1709800）
中国石油天然气股份有限公司西南油气田分公司重庆气矿科技项目/研发项目"两峡储气库集注站典型设备设施智能监测及诊断技术研究"(JS2023-239) （JS2023-239）
