Fault detection of univariate non-Gaussian data with Bayesian network

Author

Verron, Sylvain ; Tiplica, Teodor ; Kobi, Abdessamad

Author_Institution

LASQUO/ISTIA, Angers, France

fYear

2010

fDate

14-17 March 2010

Firstpage

94

Lastpage

99

Abstract

The purpose of this article is to present a new method for fault detection with Bayesian network. The interest of this method is to propose a new structure of Bayesian network allowing to detect a fault in the case of a non-Gaussian signal. For that, a structure based on Gaussian mixture model is proposed. This particular structure allows to take into account the non-normality of the data. The effectiveness of the method is illustrated on a simple process corrupted by different faults.

Keywords

Gaussian processes; belief networks; fault diagnosis; production management; quality control; Bayesian network; Gaussian mixture model; fault detection; industrial process; nonGaussian signal; product quality; univariate nonGaussian data; Artificial neural networks; Bayesian methods; Extraterrestrial measurements; Fault detection; Fault diagnosis; Humans; Manufacturing; Product safety; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Technology (ICIT), 2010 IEEE International Conference on

Conference_Location

Vi a del Mar

Print_ISBN

978-1-4244-5695-6

Electronic_ISBN

978-1-4244-5696-3

Type

conf

DOI

10.1109/ICIT.2010.5472659

Filename

5472659