Multi-label relevant vector machine based simultaneous fault diagnosis

Author

Song Chao ; Xie Lei ; Zeng Jiusun

Author_Institution

Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou, China

fYear

2013

fDate

25-27 May 2013

Firstpage

4792

Lastpage

4796

Abstract

To address the simultaneous fault diagnosis problem, a Multi-Label approach that makes use of Relevance vector machines (RVM), as the learning algorithm, is proposed to decrease the number of fault identification models and deal simultaneous fault diagnosis problems. The system is proved to be efficient by the simulation test on the Tennessee Eastman Process (TEP).

Keywords

fault diagnosis; learning (artificial intelligence); support vector machines; RVM; TEP; Tennessee Eastman process; fault identification models; learning algorithm; multilabel relevant vector machine; simultaneous fault diagnosis problems; Abstracts; Educational institutions; Electronic mail; Fault diagnosis; Metrology; Process control; Support vector machines; Multi-Label; Relevance vector machines; Support vector;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561801

Filename

6561801

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=620572