A new distributed process monitoring framework is proposed in this paper that aims to effectively decompose industrial process variables for process monitoring. Existing variable decomposition methods focus on direct correlations between variables, such as adjacency matrix, while neglecting the potential correlations. Graph embedding captures the potential correlations between variables precisely through distances in the embedding space. Consequently, the information of variables themselves used to construct graph embedding deserves greater attention. We propose a novel variable decomposition method based on raph uto-ncoder(GAE)-onnegative atrix actorization (NMF). GAE with attention mechanism is employed to derive graph embedding by taking into account the information of the variables themselves. Then, NMF with optimal modularity is run in graph embedding, and the process variables are divided into sub-blocks for distributed process monitoring. To verify the performance of the method, anonical orrelation nalysis(CCA), as a distributed process monitoring, obtains the final monitoring results. The Tennessee-Eastman process(TEP) is used to demonstrate the performance of distributed process monitoring.

中文翻译：

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通过基于图嵌入的模块化最优 NMF 分解进行分布式全厂监控


本文提出了一种新的分布式过程监控框架，旨在有效分解工业过程变量以进行过程监控。现有的变量分解方法侧重于变量之间的直接相关性，例如邻接矩阵，而忽略了潜在的相关性。图嵌入通过嵌入空间中的距离精确捕获变量之间的潜在相关性。因此，用于构造图嵌入的变量本身的信息值得更多关注。我们提出了一种基于拉夫自动编码器（GAE）-负矩阵分解（NMF）的新颖变量分解方法。采用具有注意力机制的 GAE 通过考虑变量本身的信息来导出图嵌入。然后，在图嵌入中运行具有最佳模块化的NMF，并将过程变量划分为子块以进行分布式过程监控。为了验证该方法的性能，匿名或相关分析（CCA）作为分布式过程监控，获得最终的监控结果。 Tennessee-Eastman 流程（TEP）用于演示分布式流程监控的性能。