Research on Gluing Control System Based on Support Vector Data Description and Fuzzy Control

Author

Zhang, Yizhuo ; Yu, Huiling ; Cao, Jun

Author_Institution

Northeast Forestry Univ., Harbin, China

fYear

2011

fDate

8-9 Oct. 2011

Firstpage

9

Lastpage

12

Abstract

This paper proposes a method combining support vector data description (SVDD) and fussy control to deal with the gluing procedure. First, Vibration signals are used as data of fault diagnosis, kernel principal component analysis (KPCA) is employed for feature extraction of the normal and fault examples, and SVDD algorithm as classifier is used for fault diagnosis. Second, fuzzy control rules are designed for the normal signals in order to keep the quantity of the glue with the variation of the fiber. Experiments show that SVDD algorithm is practical and efficient and the control process based on fuzzy control is stable and reliable.

Keywords

adhesives; control system synthesis; fault diagnosis; feature extraction; fibres; fuzzy control; principal component analysis; production engineering computing; signal classification; support vector machines; vibrations; wood products; SVDD algorithm; classifier; fault diagnosis; feature extraction; fiber variation; fuzzy control rules design; gluing control system; kernel principal component analysis; normal signals; support vector data description; vibration signals; Fault diagnosis; Feature extraction; Kernel; Optical fiber networks; Support vector machines; Training; Vibrations; fault diagnosis; fuzzy control; gluing system; kernel principle component analysis; support vector data description;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Acquisition and Modeling (KAM), 2011 Fourth International Symposium on

Conference_Location

Sanya

Print_ISBN

978-1-4577-1788-8

Type

conf

DOI

10.1109/KAM.2011.10

Filename

6137564