Dynamic latent‐variable (DLV) modelling is a very effective method for dynamic process monitoring. However, the DLV method only focuses on auto‐correlation in process data but ignores the cross‐correlation between inputs and outputs. To overcome this shortcoming, a novel dynamic process monitoring method using dynamic‐latent variable and canonical correlation analysis (DLV‐CCA) is proposed. Considering the dynamics in the process data, the proposed DLV‐CCA method first utilizes the DLV method to decompose the input space into input dynamic and static subspaces. The output space is also decomposed into output dynamic and static subspaces by DLV. Then, canonical correlation analysis (CCA) is used to explore the cross‐correlation between the dynamic subspaces (including input dynamic and output dynamic subspaces) and the static subspaces (including input static and output static subspaces). According to the CCA results, residual signals are generated and corresponding Hotelling's T² statistics are established to detect variations in these residual signals. A numerical example and a closed‐loop continuous stirred‐tank reactor (CSTR) are employed to demonstrate the superior performance of the DLV‐CCA based process monitoring compared with other relevant methods.

中文翻译：

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使用动态潜在变量和规范相关分析模型进行动态过程监控

动态潜变量（DLV）建模是用于动态过程监视的非常有效的方法。但是，DLV方法仅关注过程数据中的自相关，而忽略了输入和输出之间的互相关。为了克服这一缺点，提出了一种使用动态潜变量和规范相关分析（DLV-CCA）的动态过程监控方法。考虑到过程数据的动态性，提出的DLV-CCA方法首先利用DLV方法将输入空间分解为输入动态和静态子空间。DLV还可以将输出空间分解为输出动态和静态子空间。然后，规范相关分析（CCA）用于探讨动态子空间（包括输入动态子空间和输出动态子空间）与静态子空间（包括输入静态子空间和输出静态子空间）之间的互相关。根据CCA结果，会产生残留信号，并产生相应的Hotelling's建立T ²统计量以检测这些残余信号中的变化。数值实例和闭环连续搅拌釜反应器（CSTR）被用来证明与其他相关方法相比，基于DLV-CCA的过程监控具有优越的性能。