基于LSTM-JITRVM的冷轧轧制力建模方法研究

doi:10.3969/j.issn.1000-1158.2023.09.14

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摘要在带钢冷连轧生产过程中,轧制力预测准确度直接影响产品质量。为提高轧制力预测准确度,提出了基于LSTM-JITRVM(long short term memory- just in time relevance vector machine)的轧制力模型。首先,使用循环自编码网络对输入数据进行深层次特征提取,然后使用局部离群因子算法判断测试样本与其邻域点是否属于同一分布,针对不同的分布使用不同的自学习回归模型进行拟合。仿真结果表明,该模型预测准确度可控制在3%以内,能够实现轧制力的高准确度在线预测。

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	孙浩
	赵明达
	李静
	魏立新
	呼子宇

关键词 ：计量学, 轧制力预测, 冷连轧, 机器学习, 循环自编码网络, 离群因子算法

Abstract：In the process of strip cold rolling, the accuracy rolling force directly affects the product quality. In order to further improve the prediction accuracy of rolling force, a rolling force model based on LSTM-JITRVM (long short term memory- just in time relevance vector machine) is proposed. Firstly, the auto-encoding network is used to extract the deep-seated features of the input data. Then the local outlier factor algorithm is used to judge whether the test sample and its neighborhood points belong to the same distribution, and different self-learning regression models are used to fit different distributions. The simulation results show that the prediction accuracy of the model can be controlled within 3%, and the rolling force can be predicted online with high precision.

Key words： metrology rolling force prediction cold rolling machine learning cyclic self-coding network outlier factor algorithm

收稿日期: 2022-07-05 发布日期: 2023-09-21

PACS:

TB931

基金资助:国家重点研发计划(2018YFB1702300);国家自然科学基金(62003296,62073276);河北省自然科学基金(F2020203031,E2019105123);河北省高等学校科学技术研究项目(QN2020225,ZD2019311);河北省省级重点实验室绩效补助经费项目(22567612H)

作者简介: 孙浩(1985-),男, 河北霸州人, 燕山大学讲师, 博士, 主要从事进化计算、板带轧制方面的研究。Email:sunhao@ysu.edu.cn

引用本文:

孙浩,赵明达,李静,魏立新,呼子宇. 基于LSTM-JITRVM的冷轧轧制力建模方法研究[J]. 计量学报, 2023, 44(9): 1409-1416.
SUN Hao,ZHAO Ming-da,LI Jing,WEI Li-xin,HU Zi-yu. Research on Modeling Method of Cold Rolling Force Based on LSTM-JITRVM. Acta Metrologica Sinica, 2023, 44(9): 1409-1416.

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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2023.09.14 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2023/V44/I9/1409

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