SVDD Based Data-Driven Fault Detection

Yusuf Sevim [1]


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.

Process Monitoring, Fault Detection, Support Vector Data Description, Independent Component Analysis, Principal Component Analysis
  • [1] Shams M. B., Budman H. M., and Duever T. A., Fault detection, identification and diagnosis using CUSUM based PCA, Chemical Engineering Science, 66(20), 4488-4498, 2011.
  • Villegas T., Fuente M. J., and Rodríguez M., Principal component analysis for fault detection and diagnosis. experience with a pilot plant, In CIMMACS'10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics , 2010, December, pp. 147-152.
Subjects Engineering
Journal Section Research Article
Authors

Author: Yusuf Sevim
Institution: KARADENIZ TEKNIK UNIV
Country: Turkey


Dates

Publication Date : December 1, 2016

Bibtex @research article { ijamec285123, journal = {International Journal of Applied Mathematics Electronics and Computers}, issn = {}, eissn = {2147-8228}, address = {}, publisher = {Selcuk University}, year = {2016}, volume = {}, pages = {408 - 411}, doi = {10.18100/ijamec.285123}, title = {SVDD Based Data-Driven Fault Detection}, key = {cite}, author = {Sevim, Yusuf} }
APA Sevim, Y . (2016). SVDD Based Data-Driven Fault Detection. International Journal of Applied Mathematics Electronics and Computers , (Special Issue-1) , 408-411 . Retrieved from https://dergipark.org.tr/en/pub/ijamec/issue/25619/285123
MLA Sevim, Y . "SVDD Based Data-Driven Fault Detection". International Journal of Applied Mathematics Electronics and Computers (2016 ): 408-411 <https://dergipark.org.tr/en/pub/ijamec/issue/25619/285123>
Chicago Sevim, Y . "SVDD Based Data-Driven Fault Detection". International Journal of Applied Mathematics Electronics and Computers (2016 ): 408-411
RIS TY - JOUR T1 - SVDD Based Data-Driven Fault Detection AU - Yusuf Sevim Y1 - 2016 PY - 2016 N1 - DO - T2 - International Journal of Applied Mathematics Electronics and Computers JF - Journal JO - JOR SP - 408 EP - 411 VL - IS - Special Issue-1 SN - -2147-8228 M3 - UR - Y2 - 2016 ER -
EndNote %0 International Journal of Applied Mathematics Electronics and Computers SVDD Based Data-Driven Fault Detection %A Yusuf Sevim %T SVDD Based Data-Driven Fault Detection %D 2016 %J International Journal of Applied Mathematics Electronics and Computers %P -2147-8228 %V %N Special Issue-1 %R %U
ISNAD Sevim, Yusuf . "SVDD Based Data-Driven Fault Detection". International Journal of Applied Mathematics Electronics and Computers / Special Issue-1 (December 2016): 408-411 .
AMA Sevim Y . SVDD Based Data-Driven Fault Detection. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 408-411.
Vancouver Sevim Y . SVDD Based Data-Driven Fault Detection. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 411-408.