面向智能生产维护的大数据建模分析方法
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摘要
从当前的车间离散制造过程大数据应用特性出发,基于车间智能代理,实现了制造过程大数据的采集、处理分析,建立的制造过程大数据采集模型可实现对工序数据集的封装。采用基于聚类的方法对工序数据集进行离群点检测标记,实现对制造资源生产状态的描述与量化。建立了工序级数据特征模型,结合改进的相似元分析方法,对工序过程数据多层次包装描述和制造工序的相似性进行了评价。开发了车间底层智能管理系统,并将其运用于企业生产实际。
Starting from the big data application characteristics of current discrete manufacturing processes in workshops,big data collection and processing analyses of the manufacturing processes were realized based on intelligent Agent of the workshops.A big data acquisition model of the manufacturing processes was established to realize the encapsulation of process data set.A clustering-based method was used to perform the outlier detection mark on the process data set to realize the description and quantification of production states of the manufacturing resources.A process level data feature model was established,and an improved similar element analysis method was used to realize the similarity evaluation of multi-level packaging description and manufacturing process of the process data.A underlying intelligent management system of the workshop was developed and applied to the actual productions of the enterprises.
Starting from the big data application characteristics of current discrete manufacturing processes in workshops,big data collection and processing analyses of the manufacturing processes were realized based on intelligent Agent of the workshops.A big data acquisition model of the manufacturing processes was established to realize the encapsulation of process data set.A clustering-based method was used to perform the outlier detection mark on the process data set to realize the description and quantification of production states of the manufacturing resources.A process level data feature model was established,and an improved similar element analysis method was used to realize the similarity evaluation of multi-level packaging description and manufacturing process of the process data.A underlying intelligent management system of the workshop was developed and applied to the actual productions of the enterprises.
引文
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[4]周昊飞,刘玉敏.基于深度置信网络的大数据制造过程实时智能监控[J].中国机械工程,2018,29(10):1201-1207.ZHOU Yifei,LIU Yumin.Real-time Intelligent Monitoring of Big Data Manufacturing Process Based on Deep Confidence Network[J].China Mechanical Engineering,2018,29(10):1201-1207.
[5]何文韬,邵诚.工业大数据分析技术的发展及其面临的挑战[J].信息与控制,2018,47(4):398-410.HE Wenzhao,SHAO Cheng.The Development of Industrial Big Data Analysis Technology and Its Challenges[J].Information and Control,2018,47(4):398-410.
[6]杨俊刚,张洁,秦威,等.面向半导体制造的大数据分析平台[J].计算机集成制造系统,2016,22(12):2900-2910.YANG Jungang,ZHANG Jie,Qin Wei,et al.Big Data Analysis Platform for Semiconductor Manufacturing[J].Computer Integrated Manufacturing Systems,2016,22(12):2900-2910.
[7]吉卫喜,张秋菊,刘小武,等.基于WUXIASP的网络化制造资源评估与管理研究[J].制造业自动化,2005(7):27-30.JI Weixi,ZHANG Qiuju,LIU Xiaowu,et al.Research on Evaluation and Management of Networked Manufacturing Resources Based on WUXIASP[J].Manufacturing Automation,2005(7):27-30.
[8] JENNINGS N R,BUSSMANN S.Agent-based Control Systems:Why Are They Suited to Engineering Complex Systems?[J].IEEE Control Systems,2003,23(3):61-73.
[9] DAVID S J G S,THOMAS B J.A New Procedure of Clustering Based on Multivariate Outlier Detection[J].Journal of Data Science,2013,11(1):69-84.
[10]潘勇,黄进永,胡宁.可靠性概论[M].北京:电子工业出版社,2015.PAN Yong,HUANG Jinyong,HU Ning.Introduction to Reliability[M].Beijing:Publishing House of Electronics Industry,2015.
[11]张根保,李立章,冉琰,等.多品种小批量生产模式下基于相似元的工序能力分析[J].工程设计学报,2018,25(1):18-26.ZHANG Genbao,LI Lizhang,GENG Yan,et al.Analysis of Process Capability Based on Similarity Element in Multi-variety and Small Batch Production Mode[J].Journal of Engineering Design,2018,25(1):18-26.