SVDD Based Data-Driven Fault Detection

Yıl 2016, Special Issue (2016), 408 - 411, 01.12.2016

Yusuf Sevim

Öz

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.

Anahtar Kelimeler

Process Monitoring, Fault Detection, Support Vector Data Description, Independent Component Analysis, Principal Component Analysis

Kaynakça

• [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.