A multimode process monitoring method based on multiblock projection nonnegative matrix factorization (MPNMF) is proposed for traditional process monitoring methods which often adopt global model of data and ignore local information of data. Firstly, the training data set of each mode is partitioned by the complete link algorithm and the multivariate data space is divided into several subblocks. Then, the projection nonnegative matrix factorization (PNMF) algorithm is used to model each subspace of each mode separately. A joint probabilistic statistic index is defined to identify the running modes of the process data. Finally, the Bayesian information criterion (BIC) is used to synthesize the statistics of each subblock and construct a new statistic for process monitoring. The proposed process monitoring method is applied to the TE process to verify its effectiveness.

中文翻译：

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基于多块投影非负矩阵分解的多模式过程监控方法

针对传统的过程监控方法，提出了一种基于多块投影非负矩阵分解的多模式过程监控方法，该方法通常采用全局数据模型，而忽略数据局部信息。首先，通过完全链接算法对每种模式的训练数据集进行划分，并将多元数据空间划分为几个子块。然后，使用投影非负矩阵分解（PNMF）算法分别对每个模式的每个子空间建模。定义了一个联合概率统计索引以标识过程数据的运行模式。最后，使用贝叶斯信息准则（BIC）综合每个子块的统计信息，并为过程监控构建新的统计信息。