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Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(5): 1446-1454
Yinghua Yang, Xiang Shi, Xiaozhi Liu and Hongru Li, "A Novel MDFA-MKECA Method With Application to Industrial Batch Process Monitoring," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1446-1454, Sept. 2020. doi: 10.1109/JAS.2019.1911555
Citation: Yinghua Yang, Xiang Shi, Xiaozhi Liu and Hongru Li, "A Novel MDFA-MKECA Method With Application to Industrial Batch Process Monitoring," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1446-1454, Sept. 2020. doi: 10.1109/JAS.2019.1911555
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A Novel MDFA-MKECA Method With Application to Industrial Batch Process Monitoring

doi: 10.1109/JAS.2019.1911555
Funds:  This work was supported by National Key R & D Program of China (Smart process control technology for aluminum & copper strip based on industrial big data) (2017YFB0306405)
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    Abstract
  • For the complex batch process with characteristics of unequal batch data length, a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis (MDFA-MKECA) in this paper. Combining the mechanistic knowledge, different mixed data features of each batch including statistical and thermodynamics entropy features, are extracted to finish data pre-processing. After that, MKECA is applied to reduce data dimensionality and finally establish a monitoring model. The proposed method is applied to a reheating furnace industry process, and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.

     

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    Highlights

    • In this paper, a novel batch process monitoring method MDFA-MKECA is proposed for on-line process monitoring of industry process. The method achieve real-time monitoring with great performance by combining prior mechanistic knowledge and statistical and thermodynamic theory. Computational results demonstrate that the monitoring performance of MDFA-MKECA is superior to its competitors.
    • The proposed data feature extraction method, namely MDFA, can not only effectively extract the feature information of process data with complex characteristics in industrial processes, but also make up for the shortcoming of traditional multivariate statistical process monitoring that cannot handle batch data whose batch length are unequal.
    • For the reheating furnace process, we provide a more refined monitoring method based on MKECA in process monitoring, which can establish different monitoring models in differentiated physical zones to ensure that the abnormalities of the running state can be more accurately located.

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