Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

Changkyoo Yoo; Minhan Kim; Sunjin Hwang; Yongmin Jo; Jongmin Oh

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Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

Updated：2026-01-06

- Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling
- Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling
- 中国化学工程学报（英文版） 2008年16卷第1期页码：48-51
- Affiliations：
  
  College of Environmental and Applied Chemistry, Green Energy Center, Kyung Hee University, Gyeonggi,Do,Korea,446-701
- Author bio：
- Funds：
  
  the Korea Research Foundation Grant Funded by the Korean Government (MOEHRD) (KRF-2007-331-D00089);Funded by Seoul Development Institute (CS070160)
- DOI：
  中图分类号：
- 纸质出版：2008
- Accepted：
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Changkyoo Yoo, Minhan Kim, Sunjin Hwang, 等. Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling[J]. 中国化学工程学报（英文版）, 2008,16(1):48-51.

Changkyoo Yoo, Minhan Kim, Sunjin Hwang, Yongmin Jo, Jongmin Oh. Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling[J]. , 2008, 16(1): 48-51.
Changkyoo Yoo, Minhan Kim, Sunjin Hwang, 等. Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling[J]. 中国化学工程学报（英文版）, 2008,16(1):48-51. DOI：

Changkyoo Yoo, Minhan Kim, Sunjin Hwang, Yongmin Jo, Jongmin Oh. Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling[J]. , 2008, 16(1): 48-51. DOI：

摘要

Abstract

A new on-line predictive monitoring and prediction model for periodic biological processes is proposedusing the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process

and subse-quently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatmentprocess

which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept

which isthus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

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