文献类型：期刊文献

中文题名：Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

英文题名：基于邻域保留空间中模糊C聚类的多模态过程监控(英文)

作者：解翔[1];侍洪波[1]

机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes,East China University of Science and Technology,Ministry of Education

年份：2012

卷号：20

期号：6

起止页码：1174

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2011_2012】；

基金：Supported by the National Natural Science Foundation of China (61074079);Shanghai Leading Academic Discipline Project (B054)

语种：中文

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

外文关键词：模糊C-均值;子空间;过程监控;投影;监测算法;贝叶斯推理;操作条件

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.
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.