Abstract As a multivariate statistical analysis method, canonical correlation analysis (CCA) performs well for state monitoring of linear processes, but most industrial processes are nonlinear. To solve this problem, kernel canonical correlation analysis (KCCA) has been adopted; however, KCCA still has key performance indicators (KPI)-related issue. In this paper, two improved KCCA methods are proposed to deal with KPI-related issue. One is performing singular value decomposition (SVD) on the correlation coefficient matrix, then the kernel matrix can be divided into KPI-related and KPI-unrelated parts. Another one is performing general singular value decomposition (GSVD) on two coefficient matrices. In addition, this paper also performs fault detectability analysis and computational complexity analysis on these two methods. Finally, the Tennessee Eastman (TE) process is used in this study to verify the efficacy of these two proposed methods.

中文翻译：

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基于核典型相关分析的关键性能指标相关状态监测

摘要 作为一种多元统计分析方法，典型相关分析（CCA）在线性过程的状态监测方面表现良好，但大多数工业过程是非线性的。为了解决这个问题，采用了核典型相关分析（KCCA）；但是，KCCA 仍然存在与关键绩效指标 (KPI) 相关的问题。在本文中，提出了两种改进的 KCCA 方法来处理 KPI 相关问题。一种是对相关系数矩阵进行奇异值分解（SVD），然后将核矩阵分为KPI相关部分和KPI无关部分。另一种是对两个系数矩阵执行通用奇异值分解 (GSVD)。此外，本文还对这两种方法进行了故障可检测性分析和计算复杂度分析。最后，