Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 45-51.

• Review • Previous Articles     Next Articles

Study and Application of Industrial Big Data in Production Management and Control

ZHAO Ying, HOU Jun-jie, YU Cheng-long, XU Hao, ZHANG Wei   

  1. China Aerospace Academy of Systems Science and Engineering,Beijing 100048,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: To promote the application of industrial big data in smart manufacturing,related research was reviewed.According to production management and control needs,this paper started from the connotation and architecture of industrial big data,and analyzed the key technologies of industrial big data from three levels:data dynamic perception and collection,data unified storage and management,data analysis and decision support.Then,this paper introduced the application of industrial big data in quality management,fault diagnosis and forecasting,supply chain optimization and other typical scenarios.And based on a comprehensive analysis of its development status,this paper anticipated the future application trend of industrial big data.

Key words: Industrial big data, Production management and control, Smart manufacturing, Industrial cloud, Internet of things

CLC Number: 

  • TP399
[1] 郑树泉,宗宇伟,董文生,等.工业大数据架构与应用[M].上海:上海科学技术出版社,2017:30-46.
[2] 中华人民共和国工业和信息化部.工业大数据白皮书(2017版)[Z].2017.
[3] GE.Industrial internet:pushing the boundaries of minds and machines [M].Beijing:China Machine Press,2015:50-71.
[4] GE.Predix platform:the foundation for digital industrial applications[EB/OL].(2018-02-10)[2018-04-23].https://www.ge.com/digital/sites/default/files/Predix-from-GE-Digital-Over-view-Brochure.pdf.
[5] SIEMENS.MindSphere whitepaper[EB/OL].(2017-06-07)[2018-04-23].https://www.siemens.com/cn/zh/home/products/software/mindsphere.html.
[6] 王建民.工业大数据技术[J].电信网技术,2016(8):1-5.
[7] 刘东洪.基于Hadoop的机床运行状态信息采集与存储平台研究[D].重庆:重庆大学,2016.
[8] 穆化鑫.基于Storm引擎的物联网异构数据融合系统的设计与实现[D].北京:北京邮电大学,2015.
[9] RANI S,AHMED S H,TALWAR R,et al.Can sensors collect big data? an energy efficient big data gathering algorithm for a WSN[J].IEEE Transactions on Industrial Informatics,2017,13(4):1961-1968.
[10] 陈伟兴.生产过程制造物联关键事件主动感知与处理技术研究[D].贵阳:贵州大学,2016.
[11] LIU W N,ZHENG L J,SUN D H,et al.RFID-enabled real-time production management system for Loncin motorcycle assembly line[J].International Journal of Computer Integrated Manufacturing,2012,25(1):86-99.
[12] WANG L,ALEXANDER C A.Big data in design and manufacturing engineering[J].American Journal of Engineering and Applied Sciences,2015,8(2):223-232.
[13] JI C,SHAO Q S,SUN J,et al.Device data ingestion for industrial big data platforms with a case study[J].Sensors,2016,16(3),279.
[14] 罗剑明.制造物联网的实时数据感知与处理模型的研究[D].广州:广东工业大学,2015.
[15] 郑涛,刘聪颖.基于小世界网络模型的工业企业物联网数据感知网络拓扑优化[J].企业经济,2015(1):78-81.
[16] WANG L D,WANG G H.Big data in cyber physical systems,digital manufacturing and industry 4.0[J].International Jour nal of Engineering and Manufacturing,2016(6):1-8.
[17] 李毅,陆百川,李雪.基于多尺度Kalman滤波的多传感器数据融合[J].重庆交通大学学报(自然科学版),2012,31(2):299-303.
[18] BENESCH M,KUBIN H,KABITZSCH K.Processing of real-time data in big manufacturing systems[J].Procedia Manufacturing,2017,11:2114-2122.
[19] 张洁,高亮,秦威,等.大数据驱动的智能车间运行分析与决策方法体系[J].计算机集成制造系统,2016,22(5):1220-1228.
[20] 王敏.制造业大数据分布式存储管理方法研究[D].武汉:武汉大学,2017.
[21] 王卓,辛星,尹晓,等.基于Hadoop的钢铁生产大数据存储平台研究[J].软件,2016,37(9):47-51.
[22] 成飞龙.基于Hadoop的海量传感数据管理系统[D].南京:南京理工大学,2013.
[23] 张华伟,陈勇,李海斌,等.基于HBase的工业大数据时序数据存储实现[J].电信科学,2017,33(S1):21-27.
[24] 鲍远松,董文生,万英杰,等.一种基于HBase存储引擎的工业大数据前向插值方法[J].信息技术与标准化,2016(9):56-59.
[25] 李欣.基于大数据的钻井物料信息多维分析研究[D].西安:西安石油大学,2016.
[26] 冯朝阁.基于YARN的工业大数据处理平台研究与实现[D].西安:西安电子科技大学,2015.
[27] 罗俊丽.基于本体的制造资源建模方法研究[J].软件导刊,2016,15(8):4-6.
[28] 杨明耀,钱彦岭,杜凯.面向维修数据采集与质量保证的维修领域本体建模[J].兵工自动化,2015,34(1):49-55.
[29] 黄彦浩,周孝信.基于本体理论的智能电网广义数据管理模型[J].电力系统自动化,2014,38(9):114-118.
[30] XIANG F,YIN Q,WANG Z H,et al.Systematic method for big manufacturing data integration and sharing[J].International Journal of Advanced Manufac turing Technology,2018,94:3345-3358.
[31] FERRY N,TERRAZAS G,SOLBERG A,et al.Towards a big data platform for managing machine generated data in the cloud[C]∥2017 IEEE 15th International Conference on Industrial Informatics(INDIN).2017:263-270.
[32] ZHANG Y F,REN S,LIU Y,et al.A framework for big data driven product lifecycle management[J].Journal of Cleaner Production,2017,159:229-240.
[33] CHENG Y,CHEN K,SUN H M,et al.Data and knowledge mining with big data towards smart production[J].Journal of Indus trial Information Integration,2018,9:1-13.
[34] NAGORNY K,LIMA-MONTEIRO P,BARATA J,et al.Big data analysis in smart manufacturing:a review[J].International Journal of Communications Network & System Sciences,2017,10(3):31-58.
[35] 吴沛霖,何枫,仲宇,等.一种基于分布式计算平台的试验数据关联规则挖掘算法[J].信息安全与技术,2015,6(5):75-79.
[36] 陈志云,肖楚乔.基于Storm的工业流水线实时分析系统设计与实现[J].计算机应用与软件,2017,34(11):48-52.
[37] 王德文,杨力平.智能电网大数据流式处理方法与状态监测异常检测[J].电力系统自动化,2016,40(14):122-128.
[38] JAYARATNE M,ALAHAKOON D,SILVA D D,et al.Apache spark based distributed self-organizing map algorithm for sensor data analysis[C]∥Conference of the IEEE Industrial Electronics Society.IEEE,2017:8343-8349.
[39] 于青民.基于大数据分析的风力发电机健康监测研究[D].济南:山东大学,2017.
[40] VERMA A,KUSIAK A.Prediction of status patterns of wind turbines:a data-mining approach[J].Journal of Solar Energy Engineering,2011,133(1):1-10.
[41] 朱雪初,乔非.基于工业大数据的晶圆制造系统加工周期预测方法[J].计算机集成制造系统,2017,23(10):2172-2179.
[42] KU J H.A study on prediction model of equipment failure through analysis of big data based on RHadoop[J].Wireless Personal Communications,2017(12):1-14.
[43] UNAL M,SAHIN Y,ONAT M,et al.Fault diagnosis of rolling bearings using data mining techniques and boosting[J].Journal of Dynamic Systems Measurement & Control,2017,139(2):021003-021003-12.
[44] LAOUTI N,OTHMAN S,ALAMIR M,et al.Combination of model-based observer and support vector machines for fault detection of wind turbines[J].International Journal of Automation and Computing,2014,11(3):274-287.
[45] 田立瑞.面向生产制造的大数据分析平台技术研究[D].济南:山东大学,2017.
[46] 周昊飞.基于模式识别的自动化生产过程质量智能诊断研究[D].郑州:郑州大学,2016.
[47] 张一震.基于集成学习机的航空发动机气路参数预测方法研究[D].哈尔滨:哈尔滨工业大学,2017.
[48] PODRŽAJ P,ČEBULAR A.The application of LVQ neural net-work for weld strength evaluation of RF-welded plastic mate-rials[J].IEEE/ASME Transactions on Mechatronics,2016,21(2):1063-1071.
[49] 谷强,汪叔淳.智能制造系统中机器学习的研究[J].计算机工程与科学,2000(1):59-62,75.
[50] 鲁建厦,胡庆辉,董巧英.智慧制造及其研究现状[J].浙江工业大学学报,2016,44(6):681-688.
[51] TAO F,QI Q L,LIU A,et al.Data-driven smart manufacturing[J].Journal of Manufacturing Systems,2018,1:1-13.
[52] O’DONOVAN P,LEAHY K,BRUTON K,et al.Big data in manufacturing:a systematic mapping study[J].Journal of Big Data,2015,2(1):1-22.
[53] WALLER M A,FAWCETT S E.Data science,predictive ana-lytics,and big data:a revolution that will transform supply chain design and management[J].Journal of Business Logistics,2013,34(2):77-84.
[54] LIM J,CHAE M J,YANG Y,et al.Fast scheduling of semiconductor manufacturing facilities using case-based reasoning[J],IEEE Trans.Semicond.Manuf.,2016,29(1):22-32.
[55] AZADEH A,NEGAHBAN A,MOGHADDAM M.A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems [J].International Journal of Production Research,2012,50(2):551-566.
[56] 徐迭石.大数据环境下车间生产异常的智能发现与响应方法研究[D].哈尔滨:哈尔滨理工大学,2017.
[57] 张存吉.智慧制造环境下感知数据驱动的加工作业主动调度方法研究[D].广州:华南理工大学,2016.
[58] 海尔集团.海尔COSMOPlat-工业大数据平台[EB/OL].(2017-4-28)[2018-4-23].https://cosmoplat.com/solution/micro/?solutionId=132.
[59] ESMALIFALAK M.A data mining approach for fault diagnosis:An application of anomaly detection algorithm[J].Measurement,2014,55(3):343-352.
[60] RASHID M M,AMAR M,GONDAL I,et al.A data mining approach for machine fault diagnosis based on associated frequency patterns[J].Applied Intelligence,2016,45(3):1-14.
[61] KIM J,LEE D,KO D,et al.An autonomic computing based on big data platform for high-reliable smart factory[J].Information,2017,20(6):3947-3956.
[62] 黄天恩,孙宏斌,郭庆来,等.基于电网运行仿真大数据的知识管理和超前安全预警[J].电网技术,2015,39(11):3080-3087.
[63] 钟福磊.工业大数据环境下的混合故障诊断模型研究[D].西安:西安电子科技大学,2015.
[64] 胡亮,刘洋.工业大数据在航天制造领域的集成应用研究[J].军民两用技术与产品,2015(23):48-51.
[65] 刘苑红,张伟,崔艳妍,等.大数据在用户供电可靠性预测评估中的应用[J].电力信息与通信技术,2016,14(3):55-59.
[66] 武方方.基于大数据的物流配送中心选址优化研究[D].合肥:合肥工业大学,2015.
[67] 王枥珠.基于物流信息平台的数据挖掘系统的设计与实现[D].北京:北京邮电大学,2017.
[68] SAP.What’s new in SAP S/4 HANA [EB/OL].(2017-9-15)[2018-4-23].https://help.sap.com/doc/b870b6ebcd2e4b5890f16f4b06827064/1709%20000/en-US/WN_OP1709_EN.pdf.
[1] LIANG Jun-bin, TIAN Feng-sen, JIANG Chan, WANG Tian-shu. Survey on Task Offloading Techniques for Mobile Edge Computing with Multi-devices and Multi-servers in Internet of Things [J]. Computer Science, 2021, 48(1): 16-25.
[2] GAO Ji-xu, WANG Jun. Multi-edge Collaborative Computing Unloading Scheme Based on Genetic Algorithm [J]. Computer Science, 2021, 48(1): 72-80.
[3] XIE Ying-ying, SHI Jian, HUANG Shuo-kang, LEI Kai. Survey on Internet of Things Based on Named Data Networking Facing 5G [J]. Computer Science, 2020, 47(4): 217-225.
[4] JIN Hui-fang, LYU Zong-wang, ZHEN Tong. Study on New Model of Food Supply Chain Finance Based on Internet of Things+Blockchain [J]. Computer Science, 2020, 47(11A): 604-608.
[5] XU He, WU Hao, LI Peng. Design of Temporal-spatial Data Processing Algorithm for IoT [J]. Computer Science, 2020, 47(11): 310-315.
[6] ZHANG Yuan-yuan, QIN Ling. Efficient Access Control Scheme for Internet of Things Search Technology [J]. Computer Science, 2019, 46(8): 194-200.
[7] WANG Zhen, ZHOU Ying, HUANG Cheng-dong, MIAO Quan-qiang. Survey on Blockchain Solution for Big Data [J]. Computer Science, 2019, 46(6A): 6-10.
[8] XU Yong, ZHANG Xiao-rong, ZHU Yu-jun. High Speed Joining Scheme Based on Channel Evaluation for IEEE 802.15.4e TSCH [J]. Computer Science, 2019, 46(6A): 332-335.
[9] WU Bin-feng. Design of IoT Middleware Based on Microservices Architecture [J]. Computer Science, 2019, 46(6A): 580-584.
[10] BAI Ruo-chen, PANG Chen-xin, JIA Jia, QIU Shu-guang, SHAO Jia, LU Xiao-jiao. Design of Cloud Platform for Energy Internet of Things Based on LPWAN Multi-protocol [J]. Computer Science, 2019, 46(6A): 589-592.
[11] ZHANG Guang-hua, YANG Yao-hong, ZHANG Dong-wen, LI Jun. Secure Routing Mechanism Based on Trust Against Packet Dropping Attack in Internet of Things [J]. Computer Science, 2019, 46(6): 153-161.
[12] LIU Zheng-hao, ZHANG Guang-quan. Distributed Knowledge Framework of IOT Software Based on WSAN and Its Implementation Strategy [J]. Computer Science, 2019, 46(1): 148-154.
[13] YANG Wei, HE Jie, WAN Ya-dong, WANG Qin. Security for Communication Protocols in Internet of Things:A Survey [J]. Computer Science, 2018, 45(12): 32-41.
[14] LI Dong-yue, YANG Gang, QIAN Bo. Research on Architecture of Internet of Things [J]. Computer Science, 2018, 45(11A): 27-31.
[15] XIA Jun, LIU Jun-fa, JIANG Xin-long, CHEN Yi-qiang. Incremental Indoor Localization for Device Diversity Issues [J]. Computer Science, 2018, 45(10): 69-77.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .