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Distributed dictionary learning for industrial process monitoring with big data

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Abstract

With the development of sensor and communication technology, industrial systems have accumulated a large amount of data. This data has provided new perspectives and methods for industrial system analysis, monitoring and control, which is proven to be of great significance. However, with the collection and storage of industrial data in a 7 × 24 manner, the computing and information processing capabilities of edge controllers and computers at industrial sites face new challenges. Therefore, this paper proposes a distributed dictionary learning algorithm based on the MapReduce framework. The dictionary learning method can efficiently extract useful information from high-dimensional data for process monitoring. In addition, deploying the algorithm under the MapReduce framework can achieve the purpose of parallel distributed computing, which would solve the issue that the ability of calculation and information processing is limited at industrial sites. Based on extensive numerical experiments, the proposed method can improve the effectiveness and robustness of process monitoring for industrial processes.

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Acknowledgments

This work was supported in part by the the National Key R&D Program of China (2019YF- B1705300), the National Natural Science Foundation of China (Grant Nos. 61860206014), in part by the Innovation-Driven Plan in Central South University (2019CX020), and in part by the 111 Project (B17048).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Keke Huang, Ke Wei, Yonggang Li & Chunhua Yang

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  1. Keke Huang
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  2. Ke Wei
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  3. Yonggang Li
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  4. Chunhua Yang
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Correspondence to Keke Huang.

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Huang, K., Wei, K., Li, Y. et al. Distributed dictionary learning for industrial process monitoring with big data. Appl Intell 51, 7718–7734 (2021). https://doi.org/10.1007/s10489-020-02128-x

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