期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 68, 期 3, 页码 2626-2636出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.2972472
关键词
Kernel; Monitoring; Fault detection; Matrix decomposition; Correlation; Singular value decomposition; Loading; Fault detectability analysis; fault detection; kernel partial least squares (KPLS); key performance indicator (KPI); nonlinear; process monitoring
资金
- National Natural Science Foundation of China [61822308, 61751307]
- Natural Science Foundation of Shandong Province [JQ201812]
- Research Fund for the Taishan Scholar Project of Shandong Province of China
The article proposed an improved KPLS method that considers KPI-related information for KPI-related process monitoring. The method includes performing GSVD on calculable loadings based on the kernel matrix, and dividing the kernel matrix into KPI-related and KPI-unrelated subspaces. Additionally, the article presents the design of statistics for process monitoring and a detailed algorithm performance analysis for kernel methods.
Although the partial least squares approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method was proposed in the literature. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this article, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. Besides, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman benchmark process demonstrate the efficacy and merits of the improved KPLS-based method.
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