Non‐negative matrix factorization (NMF) is a novel technique for dimension‐reduction, which can be used to process data of non‐Gaussian and Gaussian efficiently. A global NMF model is inappropriate for the whole process, since it neglects the local information and monitoring results are often hard to be interpreted. On the basis of adaptive partition non‐negative matrix factorization (APNMF) and Bayesian inference, a multi‐block NMF model for non‐Gaussian process monitoring is put forward to detect and isolate the faults effectively. Using APNMF method, the original variables in different fault states can be adaptively divided into multiple sub‐blocks, and on this basis, the NMF monitoring model of each sub‐block is formed. Then, two new statistics are constructed by Bayesian inference to supply an intuitive display. Finally, a weighted reconstruction‐based contribution (RBC) plot method is presented to reduce the smearing effect and find out the main causes of these faults. This method makes full use of the local and global information of process data and improves the effectiveness of process monitoring. The validity and feasibility of the proposed method will be proved by an example of a numerical process, a Tennessee Eastman (TE) benchmark process and a continuous stirred‐tank reactor (CSTR) process.

中文翻译：

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基于自适应分区非负矩阵分解和贝叶斯推理的多块NMF非高斯过程监控模型

非负矩阵分解（NMF）是降维的一种新技术，可用于高效处理非高斯和高斯数据。全局NMF模型不适用于整个过程，因为它忽略了本地信息，并且监控结果通常难以解释。在自适应分区非负矩阵分解（APNMF）和贝叶斯推断的基础上，提出了一种用于非高斯过程监测的多块NMF模型，以有效地检测和隔离故障。使用APNMF方法，可以将不同故障状态下的原始变量自适应地划分为多个子块，并在此基础上形成每个子块的NMF监视模型。然后，通过贝叶斯推理构造两个新的统计数据以提供直观的显示。最后，提出了一种基于加权重建的贡献（RBC）绘图方法，以减少拖尾效应并找出这些故障的主要原因。该方法充分利用了过程数据的本地和全局信息，提高了过程监控的有效性。通过数值过程，田纳西·伊士曼（TE）基准过程和连续搅拌釜反应器（CSTR）过程的实例证明了该方法的有效性和可行性。