Unsteady fault diagnosis method for chemical process based on SVM

Author

Yu, Shui ; Ma, Fan-yuan ; Chen, Jian-xue ; Yin, Xing-guo ; Shi, Hong-bo

Author_Institution

Dept. of Comput. Sci. & Technol., Shanghai Jiaotong Univ., China

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

772

Abstract

Support Vector Machines (SVM) have met with significant success in numerous real-world learning tasks. This paper reports our evaluation of SVM on unsteady fault diagnosis for chemical processes such as the CSTR model. We use fixed time series data as the input space, and the target is to classify various pre-determined fault types with high accuracy and high efficiency. The adopted SVM tool is J.C.Platt´s (2000) SVM 0.54 (Matlab Toolbox). Experimental results of the CSTR model shows its effectiveness over traditional unsteady fault diagnosis methods.

Keywords

chemical technology; fault diagnosis; learning automata; pattern classification; time series; CSTR model; Matlab Toolbox; SVM; chemical process; fixed time series data; pre-determined fault types; support vector machines; unsteady fault diagnosis method; Chemical industry; Chemical processes; Chemical technology; Continuous-stirred tank reactor; Fault detection; Fault diagnosis; Machine learning; Pattern recognition; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on

Print_ISBN

0-7803-7508-4

Type

conf

DOI

10.1109/ICMLC.2002.1174485

Filename

1174485