Multimode Process Monitoring Method Based on Multiblock Projection Nonnegative Matrix Factorization

被引：0
|
作者：
Wang, Yan [1 ]
Zhao, Yu-Bo [1 ]
Li, Chuang [1 ]
Zhu, Chuan-Qian [1 ]
Han, Shuai-shuai [1 ]
Gu, Xiao-Guang [2 ,3 ]
机构：
[1] Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China
[2] Zhengzhou Normal Univ, Intelligent Mfg Big Data Platform Zhengzhou R&D C, Zhengzhou 450044, Peoples R China
[3] Nanjing Univ, Sch Business, Nanjing 210093, Peoples R China
来源：
ADVANCES IN MATHEMATICAL PHYSICS | 2020年 / 2020卷
关键词：
FAULT-DETECTION; MODEL; PCA;
D O I：
10.1155/2020/4610493
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
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.
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收藏
页数：12
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