Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis
- 作者：Yinghua Yang ; yhyang@mail.neu.edu.cn ; Yonglu Chen ; Xiaobo Chen ; Xiaozhi Liu
- 关键词：Independent component analysis ; Canonical variate analysis ; Process monitoring
- 刊名：Chemometrics and Intelligent Laboratory Systems
- 出版年：2012
- 期刊代码：21_01697439
- 类别：ch
- 出版时间：July, 2012
- 卷：116
- 期：Complete
- 页码：94-101
- 文件大小：649 K
摘要
Tennessee Eastman (TE) process is a typical multivariate chemical process. It has some characteristics of complexity and nonlinearity. Therefore, it is an ideal research platform substituted for the real industrial process whose data is difficult to be achieved. Many scholars have done a lot of studies on monitoring approaches and applied these methods on the platform. However, it is not an easy work to obtain some ideal simulation results on detecting some special faults in TE process, such as the fault 3. In this paper, an integration of canonical variate analysis and independent component analysis method (CV-ICA) is proposed. It combines the advantages of canonical variate analysis (CVA) and independent component analysis (ICA) to solve these problems. CV-ICA applies CVA to calculate the canonical variates from the process data, and then employs ICA to extract independent components (ICs). The monitoring simulation demonstrates the availability of the proposed method.