期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 27, 期 6, 页码 2688-2695出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2018.2865130
关键词
Monitoring; Matrix decomposition; Kernel; Principal component analysis; Eigenvalues and eigenfunctions; Aerospace electronics; Correlation; Bayesian inference; kernel least squares; kernel principal component analysis (KPCA); multivariate statistical process monitoring (MSPM); quality driven
资金
- National Natural Science Foundation of China [21878081]
This brief discusses a novel quality-driven principal component analysis method for statistical process monitoring. Through a nonlinear mapping, the original measurement space is mapped to another high-dimensional space, and then the relevant information between the high-dimensional space and the process quality can be obtained with the help of the kernel least squares. Based on this, the high-dimensional space will be further projected onto two mutually orthogonal high-dimensional subspaces representing quality-related part and quality-unrelated part, respectively. The traditional method is difficult to achieve this type of projection because the mapping from the original space to a high-dimensional space is hard to calculate explicitly. The main contribution is to derive the projection matrix using the knowledge of matrix theory. For each subspace, some meaningful statistical indicators are constructed to give more targeted fault information. Case studies on a numerical instance and the Tennessee Eastman process indicate that the proposed approach outperforms some of the current research results. The reliability of quality monitoring is improved through a proper space decomposition, and the fault detection rate turns to be higher due to reasonable monitoring indicators.
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