摘要
针对工业过程产生的非线性数据存在特征维数高的问题,提出一种基于核主成分分析(Kernel Principal Compo⁃nent Analysis,KPCA)和麻雀搜索算法(Sparrow Search Algorithm,SSA)优化支持向量机(Support Vector Machine,SVM)相结合的过程故障检测算法。首先,采用KPCA算法提取工业过程数据的线性和非线性特征。然后,将提取特征后的数据作为训练样本建立SVM模型,同时利用SSA算法对SVM的惩罚因子和核参数进行优化,寻找最佳分类模型。最后,将最佳的分类模型应用于测试样本进行故障检测。为了验证所提算法的分类效果,本文利用田纳西伊斯曼化工过程数据,将KPCA-SSA-SVM与SVM、KPCA-GA-SVM(Genetic Algorithm,GA)进行对比分析,验证了所提算法的高效性和优越性。
To solve the problem of high characteristic dimension of nonlinear data generated by industrial process,a process fault detection algorithm based on Kernel Principal Component Analysis(KPCA)and Sparrow Search Algorithm(SSA)which is used to optimize the parameters of Support Vector Machine is proposed.Firstly,KPCA algorithm is used to extract linear and non⁃linear features of industrial data.Secondly,the data after feature extraction is used as training samples to establish a classification SVM model,and SSA algorithm is used to optimize the kernel parameter and penalty factor of SVM.Finally,the optimized SVM model is applied to test samples for fault detection.In this paper,in order to verify the classification effect of the proposed algo⁃rithm,KPCA-SSA-SVM is compared with SVM,KPCA-GA-SVM(Genetic Algorithm,GA)by using a set of nonlinear numerical examples and Tennessee Eastman chemical process data,and the efficiency and superiority of the proposed algorithm is verified.
作者
申志
李元
SHEN Zhi;LI Yuan(College of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110142,China)
出处
《计算机与现代化》
2023年第6期15-20,32,共7页
Computer and Modernization
基金
国家自然科学基金资助项目(61673279)。
关键词
核主成分分析
麻雀搜索算法
支持向量机
非线性过程
故障检测
kernel principal component analysis
sparrow search algorithm
support vector machine
nonlinear process
fault detection
