Research and development of thick plate shape prediction system based on industrial big data

Yufei MA; Changxin LIU; Wei KONG; Jinliang DING

doi:10.11959/j.issn.2096-3750.2021.00239

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Research and development of thick plate shape prediction system based on industrial big data

Topic: Industrial Internet and Smart Manufacturing | 更新时间：2024-06-05

- Research and development of thick plate shape prediction system based on industrial big data
- Chinese Journal on Internet of Things Vol. 5, Issue 3, Pages: 39-48(2021)
- 作者机构：
  
  1. 东北大学流程工业综合自动化国家重点实验室，辽宁沈阳 110819
  2. 宝山钢铁股份有限公司中央研究院，上海 201900
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2021.00239
  CLC： TP18
- Published：30 September 2021，
  
  Published Online：2021-09，
- 稿件说明：
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YUFEI MA, CHANGXIN LIU, WEI KONG, et al. Research and development of thick plate shape prediction system based on industrial big data. [J]. Chinese journal on internet of things, 2021, 5(3): 39-48.
DOI：

YUFEI MA, CHANGXIN LIU, WEI KONG, et al. Research and development of thick plate shape prediction system based on industrial big data. [J]. Chinese journal on internet of things, 2021, 5(3): 39-48. DOI： 10.11959/j.issn.2096-3750.2021.00239.

摘要

厚板板形是衡量厚板产品质量的重要指标之一，生产中最终板形的及时预报对于调整厚板生产操作与控制具有重要的意义。实际工业生产中，厚板数据具有耦合信息多、冗余信息量大、数据呈现多源异构性等特点，结合厚板板形预报的需求，设计并开发了厚板板形预报系统。利用数据转存功能，对工业大数据进行数据过滤和数据预处理，去除数据中的耦合信息和冗余变量。利用LSTM神经网络、卷积神经网络以及3D卷积神经网络对不同维度的数据分别提取数据特征，基于最大互信息系数将特征进行融合建立集成学习预报模型，有效地解决了多源异构数据所带来的建模困难。采用国内某厚板生产线的实际工业数据进行验证，结果证明了所开发系统的有效性。

Abstract

Thick plate shape is one of the important indicators to measure the quality of thick plate products.The timely prediction of the final plate shape in production is of great significance for adjusting the operation and control of thick plate production.In actual industrial production

thick plate data has many characteristics

such as multiple coupling information

large amount of redundant information

and multi-source heterogeneity of data.Combining the needs of thick plate shape prediction

a thick plate shape prediction system was designed and developed.The data dump function was used to filter and preprocess the industrial big data to remove the coupling information and redundant variables in the data.LSTM neural network

convolutional neural network and 3D convolutional neural network were used to extract data features from data of different dimensions

and the features were fused based on the maximum mutual information coefficient to establish an integrated learning prediction model

which effectively solved the modeling difficulties caused by multi-source heterogeneous data.The actual industrial data of a domestic thick plate production line was used for verification

and the results showed the effectiveness of the developed system.

关键词

厚板板形预报模型多源异构数据系统开发

Keywords

thick plate shapeprediction modelmulti-source heterogeneous datasystem development

references

SCHAUSBERGER F, STEINBOECK A, KUGI A . Optimization-based reduction of contour errors of heavy plates in hot rolling[J]. Journal of Process Control, 2016,47: 150-160.

XIE B S, CAI Q W, YUN Y ,et al. Development of high strength ultra-heavy plate processed with gradient temperature rolling,intercritical quenching and tempering[J]. Materials Science and Engineering:A, 2017,680: 454-468.

MA X B, LIU H M, SUN J L ,et al. Impact of main drive system of 5 m wide and heavy plate mill on screw-down load deviation[J]. Engineering Failure Analysis, 2017,79: 913-927.

王国栋 . 高质量中厚板生产关键共性技术研发现状和前景[J]. 轧钢, 2019,36(1): 1-8,30.

WANG G D . Status and prospects of research and development of key common technologies for high-quality heavy and medium plate production[J]. Steel Rolling, 2019,36(1): 1-8,30.

LAUGWITZ M, SEUREN S, JOCHUM M ,et al. Development of levelling strategies for heavy plates via controlled FE models[J]. Procedia Engineering, 2017,207: 1349-1354.

SCHAUSBERGER F, STEINBOECK A, KUGI A . Feedback control of the contour shape in heavy-plate hot rolling[J]. IEEE Transactions on Control Systems Technology, 2018,26(3): 842-856.

YU W, LI G S, CAI Q W . Effect of a novel gradient temperature rolling process on deformation,microstructure and mechanical properties of ultra-heavy plate[J]. Journal of Materials Processing Technology, 2015,217: 317-326.

BASANTA-VAL P . An efficient industrial big-data engine[J]. IEEE Transactions on Industrial Informatics, 2018,14(4): 1361-1369.

WANG T, KE H X, ZHENG X ,et al. Big data cleaning based on mobile edge computing in industrial sensor-cloud[J]. IEEE Transactions on Industrial Informatics, 2020,16(2): 1321-1329.

焦四海, 丁建华, 闫博 ,等. 厚板数据中心和智能制造的实践与探索[J]. 宝钢技术, 2020(6): 8-15.

JIAO S H, DING J H, YAN B ,et al. Practice and exploration data center and intelligent manufacture of plate mill[J]. Baosteel Technology, 2020(6): 8-15.

GUO K H, XU T, KUI X Y ,et al. iFusion:towards efficient intelligence fusion for deep learning from real-time and heterogeneous data[J]. Information Fusion, 2019,51: 215-223.

NGUYEN T T, PHAM X C, LIEW A W C ,et al. Aggregation of classifiers:a justifiable information granularity approach[J]. IEEE Transactions on Cybernetics, 2019,49(6): 2168-2177.

王国栋 . 近年我国轧制技术的发展、现状和前景[J]. 轧钢, 2017,34(1): 1-8.

WANG G D . Development,current situation and prospect of Chinese steel rolling technology in recent years[J]. Steel Rolling, 2017,34(1): 1-8.

GENG D Q, ZHANG C Y, XIA C J ,et al. Big data-based improved data acquisition and storage system for designing industrial data platform[J]. IEEE Access, 2019,7: 44574-44582.

FABLET R, VIET P H, LGUENSAT R . Data-driven models for the spatio-temporal interpolation of satellite-derived SST fields[J]. IEEE Transactions on Computational Imaging, 2017,3(4): 647-657.

JIANG X X, PAN S R, LONG G D ,et al. Cost-sensitive parallel learning framework for insurance intelligence operation[J]. IEEE Transactions on Industrial Electronics, 2019,66(12): 9713-9723.

LEE S Y, TAMA B A, CHOI C ,et al. Spatial and sequential deep learning approach for predicting temperature distribution in a steel-making continuous casting process[J]. IEEE Access, 2020,8: 21953-21965.

RESHEF D N, RESHEF Y A, FINUCANE H K ,et al. Detecting novel associations in large data sets[J]. Science, 2011,334(6062): 1518-1524.

ZHANG K, LIU N, YUAN X F ,et al. Fine-grained age estimation in the wild with attention LSTM networks[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020,30(9): 3140-3152.

CAO H C, LIU H, SONG E M ,et al. Multi-branch ensemble learning architecture based on 3D CNN for false positive reduction in lung nodule detection[J]. IEEE Access, 2019,7: 67380-67391.

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