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

摘要：

　　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 threeway structural,a novel SVDD method integrating both generalized additive kernels and local models is proposed in this paper,which is Multilocal 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.

关键词： Batch process fault detection Support vector data description Generalized additive kernel Local models

作者: Huangang Wang Daoming Li Junwu Zhou Xu Wang

作者单位: Department of Automation,Tsinghua University,Beijing 100084,China State Key Laboratory of Process Automation in Mining & Metallurgy,Beijing,China Beijing Key Laboratory of Process Automation in Mining & Metallurgy,Beijing,China

会议类型: 国际会议

会议名称: 2019中国智能自动化大会(CIA,2019)

会议地点: 江苏镇江

会议语种:英文

页码: 571-579

在线出版日期: 2019-09-20（万方平台首次上网日期，不代表论文的发表时间）