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Neural representations for quality-related kernel learning and fault detection

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Abstract

Quality-related modeling and monitoring which aim at the key performance indicators have received wide attention in the research community. The widely used kernel-based methods mainly map process variables into kernel space without considering the relationship between the high-dimension features and quality indicators; therefore, the modeling performance of such transform cannot be guaranteed. For quality-related kernel learning, we propose a framework consisting of flexible neural transform and fixed kernel mapping. In this framework, neural network is used to learn representations for predicting quality indicators in the following kernel regression models. For monitoring the quality-related and quality-independent information, we present a solution for relevant subspaces decomposition and the diagnostic logic is summarized based on the quality-related and quality-independent statistics. The effectiveness of the proposed method is evaluated by simulations and real industrial-scale process.

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Data availability

The datasets used during the current study are available at http://web.mit.edu/braatzgroup/links.html.

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Funding

The authors are grateful for the support of National key research and development program of China (2021YFC2101100), and National Natural Science Foundation of China (21878081).

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Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Mei Long Road No. 130, P.O. Box 293, Shanghai, 200237, China

    Shifu Yan & Xuefeng Yan

  2. Baoshan Iron and Steel Co., Ltd., Shanghai, 201900, China

    Lihua Lv

Authors
  1. Shifu Yan
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  2. Lihua Lv
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  3. Xuefeng Yan
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Contributions

All authors contributed to the study conception and design. Data collection, analysis and experiments were performed by SY. The first draft of the manuscript was written by SY, and all authors commented on previous versions of the manuscript.

Corresponding author

Correspondence to Xuefeng Yan.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Communicated by Priti Bansal.

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Yan, S., Lv, L. & Yan, X. Neural representations for quality-related kernel learning and fault detection. Soft Comput 27, 13543–13551 (2023). https://doi.org/10.1007/s00500-022-07022-x

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  • DOI: https://doi.org/10.1007/s00500-022-07022-x

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