The aim of the work presented in this paper is to assess the ability of support vector machines (SVM) for detecting measurement faults. Two different support vector machine approaches for detecting faults are tested and compared to neural networks. The first method is based on a SVM regression model together with an analysis of the residuals whereas the second method is based on a SVM classifier. The methods were applied to a rigorous first principles based dynamic simulator of a dearomatization process.
|Title of host publication||ALSIS 2006, Finland, 2006|
|Place of Publication||Helsinki|
|Publication status||Published - 2006|
|MoE publication type||A4 Article in a conference publication|
- fault detection
- support vector machines
- dearomatization process
Nikus, M., Vermasvuori, M., Vatanski, N., & Jämsä-Jounela, S-L. (2006). Support vector machines for detection of analyzer faults- a case study. In L. Leiviskä (Ed.), ALSIS 2006, Finland, 2006 Helsinki: Suomen Automaatioseura.