Fault Detection Based on Multi-local SVDD with Generalized Additive Kernels

Support vector data description (SVDD), has attracted many researchers’ attention in statistical process monitoring. For batch process fault detection, based on the process data analysis of the three-way structural, a novel SVDD method integrating both generalized additive kernels and local models is proposed in this paper, which is Multi-local support vector data description with Generalized Additive Kernels (MLGAK-SVDD). It can obtain both the convenient on-line batch process fault detection model and the end-of-batch fault detection model at the same time. Finally, a case study based on a fed-batch penicillin fermentation process is conducted to verify the validity of the proposed MLGAK-SVDD method.