Abstract
AbstractSupport 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.KeywordsBatch process fault detectionSupport vector data descriptionGeneralized additive kernelLocal models
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