[学科发展]大数据驱动的智能制造

摘要/Abstract

摘要： 数据是未来制造业的核心要素，工业大数据分析是赋予制造“智能”的关键。系统分析了大数据驱动的智能制造的科学范式、理论方法与使能技术，阐述了应用方向与工业实践；根据“第四范式：数据密集型科学发现”，提出了“关联-预测-调控”的大数据驱动智能制造科学范式；根据数据处理流程，总结了融合处理、关联分析、性能预测与优化决策四位一体的方法体系。围绕边缘层、平台层和应用层设计大数据平台，介绍了大数据驱动智能制造的使能技术；从智能设计、计划调度、质量优化、设备运维四个角度，综述工业大数据驱动的智能制造应用现状。

关键词: 大数据, 智能制造, 关联分析, 大数据平台, 第四范式

Abstract: The industrial big data analytics is one of the most critical issues to enable the intelligent manufacturing.According to “the fourth paradigm: data-intensive scientific discovery”, the “connection-prediction-regulation” scientific paradigm of big data-driven intelligent manufacturing was proposed.According to the data processing processes, the method system of fusion processing, correlation analysis, performance prediction and optimization decision was summarized.The big data platform was designed around the edge layer, platform layer and application layer, and the enabling technology of big data-driven intelligent manufacturing was introduced. Then four typical application scenarios were illustrated and reviewed: design, planning and scheduling, quality optimization, and machinery health management.

Key words: big data, intelligent manufacturing;correlation analysis;big data platform;the fourth paradigm

中图分类号:

TP391
TH166

张洁, 汪俊亮, 吕佑龙, 鲍劲松. [学科发展]大数据驱动的智能制造[J]. 中国机械工程.

ZHANG Jie, WANG Junliang, LYU Youlong, BAO Jinsong. Big Data Driven Intelligent Manufacturing[J]. China Mechanical Engineering.

参考文献

[1]张洁, 秦威, 鲍劲松.制造业大数据[M]. 上海:上海科学技术出版社, 2016.
ZHANG Jie,QIN Wei,BAO Jinsong. Manufacturing Big Data [M]. Shanghai: Shanghai Science and Technology Publishers, 2016.
[2]王建民.工业大数据技术综述[J].大数据, 2017,3(6):3-14.
WANG Jianmin. Survey on Industrial Big Data[J].Big Data Research, 2017,3(6):3-14.
[3]HEY T , TANSLEY S , TOLLE K . The Fourth Paradigm: Data-intensive Scientific Discovery[J]. Proceedings of the IEEE, 2009, 99(8):1334-1337.
[4]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.
ZHANG Jie, GAO Liang ,QIN Wei, et al.Big-data-driven Operational Analysis and Decision-making Methodology in Intelligent Workshop[J]. Computer Integrated Manufacturing Systems,2016, 22(5): 1220-1228.
[5]TAO Fei, CHENG Jiangfeng, QI Qinglin,et al.Digital Twin-driven Product Design, Manufacturing and Service with Big Data[J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(9/12):3563-3576.
[6]IRELAND R, LIU A. Application of Data Analytics for Product Design: Sentiment Analysis of Online Product Reviews[J]. CIRP Journal of Manufacturing Science and Technology, 2018, 23: 128-144.
[7]GEIGER C , SARAKAKIS G.Data Driven Design for Reliability[C]// Reliability & Maintainability Symposium. Tucson , 2016: 15919702.
[8]TUCKER C S, KIM H M.Data-driven Decision Tree Classification for Product Portfolio Design Optimization[J].ASME J. Comput. Inf. Sci. Eng., 2009, 9(4): 041004.
[9]LU Chao, GAO Liang, LI Xinyu, et al. A Hybrid Multi-objective Grey Wolf Optimizer for Dynamic Scheduling in a Real-world Welding Industry[J]. Engineering Applications of Artificial Intelligence, 2017, 57(C):61-79.
[10]WANG J, ZHANG J, WANG X. Bilateral LSTM: a Two-dimensional Long Short-term Memory Model with Multiply Memory Units for Short-term Cycle Time Forecasting in Re-entrant Manufacturing Systems[J]. IEEE Transactions on Industrial Informatics,2018, 14(2): 748-758.
[11]鲁建厦,胡庆辉,董巧英，等. 面向云制造的混流混合车间调度问题[J]. 中国机械工程, 2017, 28(2): 191-198.
LUJiansha,HU Qinghui,DONG Qiaoying,et al. Cloud Manufacturing-oriented Mixed-model Hybrid Shop-scheduling Problem[J].China Mechanical Engineering, 2017, 28(2): 191-198.
[12]YAO J , DENG Z . Scheduling Optimization in the Mass Customization of Global Producer Services[J]. IEEE Transactions on Engineering Management, 2015, 62(4):1-13.
[13]ROKACH L , MAIMON O . Data Mining for Improving the Quality of Manufacturing:a Feature Set Decomposition Approach[J]. Journal of Intelligent Manufacturing, 2006, 17(3):285-299.
[14]QIN Wei, ZHA Dongye, ZHANG Jie. An Effective Approach for Causal Variables Analysis in Diesel Engine Production by Using Mutual Information and Network Deconvolution[J]. Journal of Intelligent Manufacturing,2018(9):1-11.
[15]王小巧, 刘明周, 葛茂根,等. 基于混合粒子群算法的复杂机械产品装配质量控制阈优化方法[J].
机械工程学报, 2016, 52(1):130-138.
WANG Xiaoqiao,LIU Mingzhou,GE Maogen, et al. Online Control Threshold Optimization for Complex Mechanical Products Assembly Process Based on Hybrid Genetic Particle Swarm Optimization[J]. Journal of Mechanical Engineering, 2016, 52(1):130-138.
[16]GUSTAVO N M , RODRIGUES H R , LUIZ F D , et al. Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis[J]. Journal of Analytical Methods in Chemistry, 2018, 2018:1-10.
[17]雷亚国, 贾峰, 周昕,等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21):49-56.
LEI Yaguo,JIA Feng,ZHOU Xin,et al.A Deep Learning-based Method for Machinery Health Monitoring with Big Data[J]. Journal of Mechanical Engineering, 2015, 51(21):49-56.
[18]江丽, 郭顺生. 基于半监督拉普拉斯特征映射的故障诊断[J].中国机械工程, 2016, 27(14):1911-1916.
JIANG Li,GUO Shunsheng. Fault Diagnosis Based on Semi-supervised Laplacian Eigenmaps[J]. China Mechanical Engineering, 2016, 27(14): 1911-1916.
[19]张晗,杜朝辉,方作为, 等.基于稀疏分解理论的航空发动机轴承故障诊断[J].机械工程学报,2015(1):97-105.
ZHANG Han,DU Zhaohui,FANG Zuowei,et al.Sparse Decomposition Based Aero-engine's Bearing Fault Diagnosis[J].Journal of Mechanical Engineering,2015(1):97-105.
[20]SCHLECHTINGEN M , SANTOS I F . Wind Turbine Condition Monitoring Based on SCADA Data Using Normal Behavior Models. Part 2: Application Examples[J]. Applied Soft Computing, 2014, 14:447-460.
[21]GONDAL I , AMAR M , WILSON C . Vibration Spectrum Imaging:a Novel Bearing Fault Classification Approach[J]. IEEE Transactions on Industrial Electronics, 2014, 62(99):494-502.