Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace<sup>*</sup>

解翔; 侍洪波

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

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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^*
- 中国化学工程学报（英文版） 2012年20卷第6期页码：1174-1179
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education,Shanghai,China,200237
- Author bio：
- Funds：
  
  Supported by the National Natural Science Foundation of China (61074079);Shanghai Leading Academic Discipline Project (B054)
- DOI：
  中图分类号：
- 纸质出版：2012
- Accepted：
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解翔, 侍洪波. Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1174-1179.

[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1174-1179.
解翔, 侍洪波. Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1174-1179. DOI：

[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1174-1179. DOI：

摘要

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.

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