Title :
Fault detection of univariate non-Gaussian data with Bayesian network
Author :
Verron, Sylvain ; Tiplica, Teodor ; Kobi, Abdessamad
Author_Institution :
LASQUO/ISTIA, Angers, France
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;
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
DOI :
10.1109/ICIT.2010.5472659
