SVDD Based Data-Driven Fault Detection

Yusuf SEVİM

Makale detayları

SVDD Based Data-Driven Fault Detection

Yazarlar:

Yusuf SEVİM

Yayıncı:

Selcuk University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Applied Mathematics Electronics and Computers 2016 Issue Special Issue-1, pp.408-411

Belge Tipi:

Makale

Yayın Tarihi:

11/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000476029

Sıklık:

Üç Aylık

eISSN:

2147-8228

pISSN:

2147-8228

Özet:

 


Conventional data driven process monitoring algorithms are limited to
Gaussian process data for principal component analysis (PCA) algorithm and
non-Gaussian process data for independent component analysis (ICA) algorithm.
This paper provides a comparison study between the conventional data driven
methods and support vector data description (SVDD) algorithm for fault
detection (FD). Different from the traditional methods, SVDD algorithm has no
Gausssian assumption. Thus the distribution of process data is not important
for SVDD method. In order to compare their FD performances of the proposed
methods from the application viewpoint, Tennessee Eastman (TE) benchmark
process is utilized to compare the results of all the discussed methods.
Simulation results on TE process show that ICA and SVDD methods perform better
for false faults than the PCA method.

.

Makale detayları

SVDD Based Data-Driven Fault Detection

Yazarlar: Yusuf SEVİM

Yayıncı: Selcuk University

Ana Başlıklar: Uygulamalı fizik

Dergi: International Journal of Applied Mathematics Electronics and Computers 2016 Issue Special Issue-1, pp.408-411

Belge Tipi: Makale

Yayın Tarihi: 11/2016

Belge Dili: Türkçe

Document ID (Org No.): 2000476029

Sıklık: Üç Aylık

eISSN: 2147-8228

pISSN: 2147-8228

Sayı

Yazarlar:

Yusuf SEVİM

Yayıncı:

Selcuk University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Applied Mathematics Electronics and Computers 2016 Issue Special Issue-1, pp.408-411

Belge Tipi:

Makale

Yayın Tarihi:

11/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000476029

Sıklık:

Üç Aylık

eISSN:

2147-8228

pISSN:

2147-8228