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Multimode Process Monitoring Method Based on Multiblock Projection Nonnegative Matrix Factorization

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Listed:
  • Yan Wang
  • Yu-Bo Zhao
  • Chuang Li
  • Chuan-Qian Zhu
  • Shuai-shuai Han
  • Xiao-Guang Gu

Abstract

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.

Suggested Citation

  • Yan Wang & Yu-Bo Zhao & Chuang Li & Chuan-Qian Zhu & Shuai-shuai Han & Xiao-Guang Gu, 2020. "Multimode Process Monitoring Method Based on Multiblock Projection Nonnegative Matrix Factorization," Advances in Mathematical Physics, John Wiley & Sons, vol. 2020(1).
    • Handle: RePEc:wly:jnlamp:v:2020:y:2020:i:1:n:4610493
    DOI: 10.1155/2020/4610493
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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