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Statistical Analysis and Adaptive Technique for Dynamical Process Monitoring by Yingwei Zhang; Zhiming Li; Hong Zhou is a Engineering article available to read on EtoBox.
What is Statistical Analysis and Adaptive Technique for Dynamical Process Monitoring about?
Multivariate statistical process monitoring (MSPM) methods based on two-dimensional dynamic kernel PCA (2-D-DKPCA) and two-dimensional dynamic kernel Hebbian Algorithm (2-D-DKHA) are proposed. First, a nonlinear batch process monitoring scheme based on 2-D-DKPCA is proposed. Its basic idea is to use KPCA to depict the both withinbatch dynamics and batch-to-batch dynamics. However, the proposed 2-D-DKPCA needs to store the whole kernel matrix and calculate all nonlinear components. Kernel matrix will thus become extremely huge when the numbers of successive batches and samples are large. Then, kernel Hebbian Algorithm (KHA) is introduced to 2-D-DKPCA to construct 2-D-DKHA. KHA can extract adaptively nonlinear principal components without storing and manipulating the whole kernel matrix and only calculate the principal components. Thus, proposed 2-D-DKHA has the ability of monitoring complex batch processes. The 2-D-DKPCA and 2-D-DKHA are first proposed in this article. Also, from the proposed 2-D method, it is easily to obtain the 1-D algorithm. The proposed method 2-D-DKPCA is applied to the fault detection in a nonlinear dynamic system and compared with 2-D dynamic PCA (2-D-DPCA).
Who reads Statistical Analysis and Adaptive Technique for Dynamical Process Monitoring?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Yingwei Zhang; Zhiming Li; Hong Zhou
- Publisher
- Institution of Chemical Engineers; Elsevier ; Institute of Chemical Engineers; Elsevier BV (ISSN 0263-8762)
- Published
- 2010
- Language
- EN
- Field
- Engineering (Physical Sciences)