Title :
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
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;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561801
