工程科学学报2017，Vol.39Issue(1)：39-47,9.DOI:10.13374/j.issn2095-9389.2017.01.005
基于全局优化支持向量机的多类别高炉故障诊断
Multi-class fault diagnosis of BF based on global optimization LS-SVM
摘要
Abstract
Aiming at the requirement of high speed and precision in blast furnace fault diagnosis systems,a new strategy based on global optimization least-squares support vector machines (LS-SVM) was proposed to solve this problem.Firstly,the variable metric discrete particle swarm optimization algorithm was employed to optimize the feature selection and LS-SVM parameters.Secondly,the feature vector was compressed by kernel principal component analysis.Finally,the heuristic error correcting output codes were constructed on the basis of Fisher linear discriminate rate.In the fault diagnosis scheme,fewer LS-SVM classifiers were applied through meaningful partitions and recombination of fault training samples.Simulation results show that the proposed fault diagnosis method can not only improve the fault detection accurate rate,but also enhance the timeliness of the entire system.关键词
高炉/故障诊断/最小二乘分析/支持向量机/全局优化Key words
blast furnaces/fault diagnosis/least-squares analysis/support vector machines/global optimization分类
矿业与冶金引用本文复制引用
张海刚,张森,尹怡欣..基于全局优化支持向量机的多类别高炉故障诊断[J].工程科学学报,2017,39(1):39-47,9.基金项目
国家自然科学基金资助项目(61333002,61673056) （61333002,61673056）
