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A Data-Driven Online Multimodal Identification Method for Industrial Processes Based on Complex Network

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Abstract

With the increasing complexity of industrial processes, many practical industrial processes have multimodal characteristics to meet production requirements. In addition to stable modes with different operating points, transition modes are also generated when mode switching occurs. To monitor the running state of the process in real-time, it is necessary to carry out online multimodal identification. At present, how to accurately identify the transition modes and unknown modes is still an open problem. In this paper, an online multimodal identification method based on the complex network is proposed, which identifies different modes by the difference of data distribution between samples. To identify transition modes, moving window technology is introduced to extract dynamic characteristics of the historical data. The multimodal data is mapped in a complex network with the mean vectors of windows as nodes and the Jensen–Shannon (JS) divergence between the mean vectors as edges. Then, the community clustering algorithm is applied to cluster the nodes, which can automatically determine the number of clustering and solve the problem of unknown modal numbers in historical data. On this basis, the Kernel Density Estimation method is used to introduce the JS divergence thresholds to realize a new online multimodal identification, including transition modes and unknown modes. The model can be updated by collecting samples of unknown modes from the online multimodal identification results. Finally, the effectiveness of the proposed method is verified by a numerical example and the Tennessee-Eastman process.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (U21A20475, U1908213), Natural Science Foundation of Hebei Province of China (E2022501017), Fundamental Research Funds for the Central Universities (N2223001), Colleges and Universities in Hebei Province Science Research Program (QN2020504).

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Authors and Affiliations

  1. School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China

    LuLu Dong, Qiang Zhao & Libin Chen

  2. School of Computer and Communication Engineering, The State Key Laboratory of Rolling and Automation, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China

    Yinghua Han

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  1. LuLu Dong
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  2. Qiang Zhao
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  3. Libin Chen
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  4. Yinghua Han
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Contributions

LD: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. QZ: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. LC Data curation, Formal analysis, Software, Visualization. YH: Conceptualization, Resources, Supervision, Writing - review & editing.

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Correspondence to Qiang Zhao.

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Dong, L., Zhao, Q., Chen, L. et al. A Data-Driven Online Multimodal Identification Method for Industrial Processes Based on Complex Network. J Control Autom Electr Syst 34, 276–288 (2023). https://doi.org/10.1007/s40313-022-00971-6

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  • DOI: https://doi.org/10.1007/s40313-022-00971-6

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