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首页> 外文会议>American Control Conference >Quality-relevant fault detection of nonlinear processes based on kernel concurrent canonical correlation analysis
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Quality-relevant fault detection of nonlinear processes based on kernel concurrent canonical correlation analysis

机译：基于核并发规范相关分析的非线性过程质量相关故障检测

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Canonical correlation analysis (CCA) has been used for concurrent quality and process monitoring to extract multidimensional correlation structure between process and quality variables. In this paper, a new kernel concurrent CCA (KCCCA) algorithm is proposed for quality-relevant nonlinear process monitoring, which decomposes the original space into five subspaces, including correlation subspace, quality-principal subspace, quality-residual subspace, process-principal subspace and process-residual subspace. The proposed KCCCA considers the nonlinearity in both process and quality variables, and incorporates a regularization term as well for numerical robustness. In the case studies, the Tennessee Eastman process is employed to demonstrate the effectiveness of the proposed KCCCA.
机译：典型相关分析（CCA）已用于并发质量和过程监控，以提取过程和质量变量之间的多维相关结构。提出了一种新的内核并发CCA算法（KCCCA），用于质量相关的非线性过程监控，该算法将原始空间分解为五个子空间，包括相关子空间，质量主子空间，质量余子空间，过程主子空间。和过程剩余子空间。提出的KCCCA考虑了过程变量和质量变量中的非线性，并为数字鲁棒性引入了正则化项。在案例研究中，使用田纳西州伊士曼（Tennessee Eastman）流程来证明拟议中的KCCCA的有效性。

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