Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace*

Chin.J.Chem.Eng. ›› 2012, Vol. 20 ›› Issue (6): 1174-1179.

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Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*

XIE Xiang, SHI Hongbo

Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China

Received:2012-05-29 Revised:2012-07-26 Online:2012-12-28 Published:2012-12-28
Supported by:
Supported by the National Natural Science Foundation of China (61074079);Shanghai Leading Academic Discipline Project (B054)

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*

解翔, 侍洪波

Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China

通讯作者: SHI Hongbo,E-mail:hbshi@ecust.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (61074079);Shanghai Leading Academic Discipline Project (B054)

Abstract

Abstract: For complex industrial processes with multiple operational conditions,it is important to develop effective monitoring algorithms to ensure the safety of production processes.This paper proposes a novel monitoring strategy based on fuzzy C-means.The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection.Then the scores in the novel subspace are classified into several overlapped clusters,each representing an operational mode.The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index.The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

Key words: multimode process monitoring, fuzzy C-means, locality preserving projection, integrated monitoring index, Tennessee Eastman process

摘要： For complex industrial processes with multiple operational conditions,it is important to develop effective monitoring algorithms to ensure the safety of production processes.This paper proposes a novel monitoring strategy based on fuzzy C-means.The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection.Then the scores in the novel subspace are classified into several overlapped clusters,each representing an operational mode.The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index.The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

关键词: multimode process monitoring, fuzzy C-means, locality preserving projection, integrated monitoring index, Tennessee Eastman process

XIE Xiang, SHI Hongbo. Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*[J]. Chin.J.Chem.Eng., 2012, 20(6): 1174-1179.

解翔, 侍洪波. Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1174-1179.

References

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Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*

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