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Real-time monitoring of chemical processes based on variation information of principal component analysis model

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  • Bei Wang

    (East China University of Science and Technology)

  • Xuefeng Yan

    (East China University of Science and Technology)

Abstract

In industrial processes, the change of operating condition can obviously affect the relations among process data, which in turn indicate the corresponding operating conditions. Considering that the loadings and eigenvalues, generated from the principal component analysis (PCA) model, contain primary data information and can reflect the characteristics of data, this article proposes novel monitoring statistics which quantitatively evaluate the variation of these two matrices, collected from real-time updated PCA model for process monitoring. Given that abnormal data may be submerged by normal data, a combined moving window which selects both real-time data and normal data is employed to collect data for model construction. By comparing with other PCA-based and non-PCA-based methods through a simple numerical simulation and the Tennessee Eastman process, the proposed data-driven method is demonstrated to be effective and feasible. Additionally, some other PCA-based methods are utilized for comparison.

Suggested Citation

  • Bei Wang & Xuefeng Yan, 2019. "Real-time monitoring of chemical processes based on variation information of principal component analysis model," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 795-808, February.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:2:d:10.1007_s10845-016-1281-3
    DOI: 10.1007/s10845-016-1281-3
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    References listed on IDEAS

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    1. Bersimis, Sotiris & Psarakis, Stelios & Panaretos, John, 2006. "Multivariate Statistical Process Control Charts: An Overview," MPRA Paper 6399, University Library of Munich, Germany.
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    Cited by:

    1. Saideep Nannapaneni & Sankaran Mahadevan & Abhishek Dubey & Yung-Tsun Tina Lee, 2021. "Online monitoring and control of a cyber-physical manufacturing process under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1289-1304, June.

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