摘要
本项目团队在对S钢铁厂智能制造工业4.0的升级改造项目中,研究了大量的实测历史样本数据和传感器采集的实时数据,探索了企业在大数据云平台工业发展前期,对板带的整个工序全流程作业从单变量监控向多变量监控转变的质量数据处理工具的需求。本文通过运用改进的Z-C4.5决策树算法来辅助相应工艺专家进行全流程质量数据的筛选,帮助他们进行后续进一步的分析奠定基础。
The project team for 4.0 S steel intelligent manufacturing industry upgrading projects,research a lot of the measured samples of history data and sensors to collect real-time data,explores the enterprise in the big data cloud platform at the beginning of the industrial development,the on board with the whole process of the whole process from single variable control to multivariable control the quality of the data processing tool needs.In this paper,the improved Z-C4.5 decision tree algorithm is used to assist the corresponding process experts to screen the quality data of the whole process,helping them to lay a foundation for further analysis.
作者
赵峰
尹琛
吴玉国
陈梦凯
李轶
ZHAO Feng;YIN Chen;WU Yu-guo;CHEN Meng-kai;LI Yi(School of Management Science and Engineering,Anhui University of Technology,Maanshan 243032,China;Key Laboratory of Multidisciplinary Management and Control of Complex Systems,Anhui University of Technology,Maanshan 243032,China;Maanshan Tobacco Monopoly Administration,Maanshan 243032,China)
出处
《南阳理工学院学报》
2022年第2期21-28,共8页
Journal of Nanyang Institute of Technology
基金
国家自然科学基金项目(71872002)
安徽省高校人文社会科学研究重点项目(SK2019A0072)。
关键词
大数据分析
智能制造
全流程质量管理
决策树算法
big data analysis
intelligent manufacturing
whole process quality control
decision tree algorithm
