Fault Diagnosis of Lead-Zinc Smelting Furnace based on Multi-Class Support Vector Machines

Author

Jiang, Shaohua ; Gui, Weihua ; Yang, Chunhua ; Xie, Yongfang

Author_Institution

Central South Univ., Changsha

fYear

2007

fDate

May 30 2007-June 1 2007

Firstpage

1643

Lastpage

1648

Abstract

Support vector machine (SVM) is powerful for the problem with small sampling, nonlinear and high dimension. A multi-class SVM classifier is applied to fault diagnosis of imperial smelting furnace in this paper. The input data is preprocessed through a special method based on the data reliability analysis technology, and six features are extracted as the input to multiple fault classifier for identify faults, which adapt an improved ´one to others´ algorithm. The real application results show that the classifier has an excellent performance on training speed and reliability.

Keywords

blast furnaces; fault diagnosis; lead; production engineering computing; smelting; support vector machines; zinc; data reliability analysis; fault diagnosis; lead-zinc smelting furnace; multiclass SVM classifier; support vector machine; Algorithm design and analysis; Data analysis; Data preprocessing; Fault diagnosis; Feature extraction; Furnaces; Sampling methods; Smelting; Support vector machine classification; Support vector machines; Data reliability analysis; Fault diagnosis; Multi-class SVM classifier; Support vector machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2007. ICCA 2007. IEEE International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4244-0818-4

Electronic_ISBN

978-1-4244-0818-4

Type

conf

DOI

10.1109/ICCA.2007.4376639

Filename

4376639