Multimodal process monitoring based on transition-constrained Gaussian mixture model

• 作者: Qingchao Jiang Shutian Chen Xuefeng Yan
• 来源期刊: 中国化学工程学报（英文版）
• 年，卷（期）: 2020,（12）
• 所属期刊栏目: Process Systems Engineering and Process Safety
• 页码范围: 3070-3078
• 页数: 9页
• 语种: 英文

摘要

Reliable process monitoring is important for ensuring process safety and product quality. A production process is generally characterized by multiple operation modes, and monitoring these multimodal processes is challenging. Most multimodal monitoring methods rely on the assumption that the modes are independent of each other, which may not be appropriate for practical application. This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring. This technique can reduce falsely and fre- quently occurring mode transitions by considering the time series information in the mode identification of his- torical and online data. This process enables the identified modes to reflect the stability of actual working conditions, improve mode identification accuracy, and enhance monitoring reliability in cases of mode overlap. Case studies on a numerical simulation example and simulation of the penicillin fermentation process are pro- vided to verify the effectiveness of the proposed approach in multimodal process monitoring with mode overlap.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Shutian Chen		1	0	0.0	0.0
2	Qingchao Jiang		1	0	0.0	0.0
3	Xuefeng Yan		1	0	0.0	0.0