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

作　　者：张海刚[1,2] 张森[1,2] 尹怡欣[1,2] 

机构地区：[1]北京科技大学自动化学院,北京100083 [2]北京科技大学钢铁流程先进控制教育部重点实验室,北京100083

出　　处：《工程科学学报》2017年第1期39-47,共9页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(61333002;61673056)

摘　　要：针对高炉故障诊断系统快速性和准确性的要求,提出基于全局优化最小二乘支持向量机的策略.首先,采用变尺度离散粒子群对最小二乘支持向量机的参数和故障特征的选取进行优化;然后,利用核主元分析法对选取的特征向量进行压缩整理;最后,构造了以Fisher线性判别率为标准的启发式纠错输出编码.仿真结果表明,通过对故障训练样本有意义地分割重组,用较少的最小二乘支持向量机分类器,得到较高的故障判断准确率且增强了整个系统的实时性.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 

分 类 号：TF549[冶金工程—钢铁冶金]