Online Monitoring of Chemical Process Based on Adaptive Principal Component Analysis
[1]Lu Zhaomin,Zhou leather,Miao Chen.Online Monitoring of Chemical Process Based on Adaptive Principal Component Analysis[J].Journal of Zhengzhou University (Engineering Science),2020,41(01):44-48.[doi:10.13705/j.issn.1671-6833.2019.04.006]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41卷
Number of periods:
2020 01
Page number:
44-48
Column:
Public date:
2020-03-10
- Title:
- Online Monitoring of Chemical Process Based on Adaptive Principal Component Analysis
- Keywords:
- process monitoring; principal component analysis; subspace; adaptive
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2019.04.006
- Abstract:
- When Principal Component Analysis (PCA) is applied to process monitoring, improper component selection method will cause variation characteristics to be dispersed or submerged, thus affecting monitoring performance. In order to solve this problem, this paper proposes an adaptive selection method of components and applies it to process monitoring, called Adaptive Principal Component Analysis (APCA). The application of adaptive principal components to process monitoring mainly includes three steps: 1 ) firstly, calculating the similarity of each component based on the load matrix through Euclidean distance during offline modeling, and selecting components with high similarity to each component to form multiple molecular spaces 2 ) Secondly, during on-line monitoring, the variation probability of each component is calculated by kernel density estimation based on each component of the on-line sample, and the component with the highest variation probability is selected as the characteristic component (CC) 3 ) Finally, the molecular space corresponding to CC is selected and statistics are constructed. Numerical simulation and Tennessee Eastman (TE) process prove the effectiveness of the proposed APCA
- References:
- -
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Last Update:
2020-02-22