A new fuzzy membership function for FSVM and its application in machinery fault diagnosis

Author

Tang, Hao ; Liao, Yuhe ; Wang, Xiufeng

Author_Institution

CISDI R&D Co., Ltd., Chongqing, China

fYear

2012

fDate

29-31 May 2012

Firstpage

35

Lastpage

39

Abstract

In this paper, a new fuzzy membership function for fuzzy support vector machine is presented. It provides an effective approach to deal with the over-fitting problem when outliers exist in the training data set. Combining with the concept of the K-nearest neighbor algorithm, we give a definition of the new fuzzy membership function. Then, fuzzy support vector machine with some improvements is successfully applied in machinery fault diagnosis and some engineering experimental results show the good performance of the present approach.

Keywords

fault diagnosis; fuzzy set theory; machinery; mechanical engineering computing; support vector machines; FSVM; fuzzy membership function; fuzzy support vector machine; k-nearest neighbor algorithm; machinery fault diagnosis; over-fitting problem; Fault diagnosis; Kernel; Support vector machines; Testing; Training; Valves; fuzzy membership function; fuzzy support vector machine; machinery fault diagnosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2012 Eighth International Conference on

Conference_Location

Chongqing

ISSN

2157-9555

Print_ISBN

978-1-4577-2130-4

Type

conf

DOI

10.1109/ICNC.2012.6234682

Filename

6234682