DocumentCode :
550321
Title :
Online process monitoring based on incremental LPP
Author :
Zeng Jiusun ; Gao Chuanhou ; Luo Shihua ; Li Qihui
Author_Institution :
Coll. of Metrol. & Meas. Eng., China Jiliang Univ., Hangzhou, China
fYear :
2011
fDate :
22-24 July 2011
Firstpage :
4200
Lastpage :
4204
Abstract :
Process monitoring by manifold learning has become an important research area. This paper proposes an online process monitoring scheme based on incremental locality preserving projection (LPP). As new data sample arrives, the algorithm makes use of the previous computation results to update the neighbor structure; and also by using the eigenvectors at last time step as the initial vector of the Raleigh quotient iteration, thus achieves higher efficiency. The incremental LPP is then used to construct process monitoring model for blast furnace ironmaking process. Application results show that the proposed method can efficiently track the time-varying characteristics of the process, discover faults of the process and reduce false alarms.
Keywords :
eigenvalues and eigenfunctions; learning (artificial intelligence); process monitoring; production engineering computing; Raleigh quotient iteration; blast furnace ironmaking process; eigenvectors; incremental LPP; incremental locality preserving projection; manifold learning; neighbor structure; online process monitoring; time-varying characteristics; Electronic mail; Fault detection; Laplace equations; Monitoring; Presses; Process control; USA Councils; Incremental LPP; Manifold learning; Process Monitoring;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
ISSN :
1934-1768
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768
Type :
conf
Filename :
6000659
Link To Document :
بازگشت