Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

钢铁研究学报(英文版), 2011, 18(10): 17-33.

Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

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1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China

2.College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China

3.College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China

4.College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China

基金项目: ltem Sponsored by National Natural Science Foundation of China(60843007;61050006)

关键词：快速故障诊断 , SVM算法 , 成本意识 , 分类方法 , CLS , 高炉 , 离散粒子群优化 , 最小二乘支持向量机

Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

Keywords： blast furnace , fault diagnosis , eosc-conscious , LS-SVM , multi-class classification ,

Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine （LS-SVM） is proposed to solve this problem. Firstly, modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Secondly, cost-con- scious formula is presented for fitness function and it contains in detail training time, recognition accuracy and the feature selection. The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier. The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general- ization performance in the application of fault diagnosis of the blast furnace. Thirdly, a gradual change binary tree is established for blast furnace faults diagnosis. It is a multi-class classification method based on center-of-gravity formula distance of cluster. A gradual change classification percentage ia used to select sample randomly. The proposed new metbod raises the sped of diagnosis, optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace.

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Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

参考文献