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Local multi-model integrated soft sensor based on just-in-time learning for mechanical properties of hot strip mill process

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Abstract

The mechanical properties of hot rolled strip are the key index of product quality, and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process. To solve the problem that it is difficult to measure the mechanical properties of hot rolled strip in time and accurately, a soft sensor based on ensemble local modeling was proposed. Firstly, outliers of process data are removed by local outlier factor. After standardization and transformation, normal data that can be used in the model are obtained. Next, in order to avoid redundant variables participating in modeling and reducing performance of models, feature selection was applied combing the mechanism of hot rolling process and mutual information among variables. Then, features of samples were extracted by supervised local preserving projection, and a prediction model was constructed by Gaussian process regression based on just-in-time learning (JITL). Other JITL-based models, such as support vector regression and gradient boosting regression tree models, keep all variables and make up for the lost information during dimension reduction. Finally, the soft sensor was developed by integrating individual models through stacking method. Superiority and reliability of proposed soft sensors were verified by actual process data from a real hot rolling process.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (NSFC) under Grants 61773053 and 61873024 and Fundamental Research Funds for the China Central Universities of USTB (FRF-TP-19-049A1Z). Also, thanks are given for the National Key R&D Program of China (No. 2017YFB0306403) for funding.

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Correspondence to Kai-xiang Peng.

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Dong, J., Tian, Yz. & Peng, Kx. Local multi-model integrated soft sensor based on just-in-time learning for mechanical properties of hot strip mill process. J. Iron Steel Res. Int. 28, 830–841 (2021). https://doi.org/10.1007/s42243-021-00611-4

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  • DOI: https://doi.org/10.1007/s42243-021-00611-4

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