The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action

Digital twins, Internet of Things (IoT), block chains, and Artificial Intelligence (AI) may redefine our imagination and future vision of globalization. Digital Twin will likely affect most of the enterprises worldwide as it duplicates the physical model for remote monitoring, viewing, and controlling based on the digital format. It is actually the living model of the physical system which continuously adapts to operational changes based on the real-time data from various IoT sensors and devices and forecasts the future of the corresponding physical counterparts with the help of machine learning/artificial intelligence. We have investigated the architecture, applications, and challenges in the implementation of digital twin with IoT capabilities. Some of the major research areas like big data and cloud, data fusion, and security in digital twins have been explored. AI facilitates the development of new models and technology systems in the domain of intelligent manufacturing.

[1]  Tom Ziemke,et al.  On the Definition of Information Fusion as a Field of Research , 2007 .

[2]  Hideo Setoya History and review of the IMS (Intelligent Manufacturing System) , 2011, 2011 IEEE International Conference on Mechatronics and Automation.

[3]  J. Madejski Survey of the agent-based approach to intelligent manufacturing , 2007 .

[4]  Ulf Lindqvist,et al.  An intrusion detection system for wireless process control systems , 2008, 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[5]  Igor Nai Fovino,et al.  A Multidimensional Critical State Analysis for Detecting Intrusions in SCADA Systems , 2011, IEEE Transactions on Industrial Informatics.

[6]  Fernando González-Ladrón-de-Guevara,et al.  Towards an integrated crowdsourcing definition , 2012, J. Inf. Sci..

[7]  Samir I. Shaheen,et al.  Simple, Flexible, and Interoperable SCADA System Based on Agent Technology , 2015, ArXiv.

[8]  Stephen Hailes,et al.  Security of smart manufacturing systems , 2018 .

[9]  Ibrar Yaqoob,et al.  A survey of big data management: Taxonomy and state-of-the-art , 2016, J. Netw. Comput. Appl..

[10]  Sankaran Mahadevan,et al.  A dynamic Bayesian network approach for digital twin , 2017 .

[11]  László Monostori,et al.  Agent-based systems for manufacturing , 2006 .

[12]  Om Pal,et al.  Cryptographic Key Management for SCADA System: An Architectural Framework , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[13]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[14]  James Llinas,et al.  Handbook of Multisensor Data Fusion : Theory and Practice, Second Edition , 2008 .

[15]  Wang Su-qing Noisy-data-disposing Algorithm of Data Clean on the Attribute Level , 2005 .

[16]  Fei Tao,et al.  New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  Pingyu Jiang,et al.  Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information , 2019, J. Intell. Manuf..

[18]  Qun Li,et al.  A Survey of Fog Computing: Concepts, Applications and Issues , 2015, Mobidata@MobiHoc.

[19]  Fei Tao,et al.  Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.

[20]  Roland Rosen,et al.  About The Importance of Autonomy and Digital Twins for the Future of Manufacturing , 2015 .

[21]  Kawuu W. Lin,et al.  A novel parallel algorithm for frequent pattern mining with privacy preserved in cloud computing environments , 2010, Int. J. Ad Hoc Ubiquitous Comput..

[22]  C. Lynch Big data: How do your data grow? , 2008, Nature.

[23]  Mostafaeipour Ali,et al.  Implementation of Web based Technique into the Intelligent Manufacturing System , 2011 .

[24]  F Tao,et al.  Theories and technologies for cyber-physical fusion in digital twin shop-floor , 2017 .

[25]  David Alan Bourne,et al.  Manufacturing intelligence , 1988 .

[26]  Eric Tuegel,et al.  Challenges with Structural Life Forecasting Using Realistic Mission Profiles , 2012 .

[27]  Yingfeng Zhang,et al.  Real-time information capturing and integration framework of the internet of manufacturing things , 2015, Int. J. Comput. Integr. Manuf..

[28]  Jiafu Wan,et al.  Implementing Smart Factory of Industrie 4.0: An Outlook , 2016, Int. J. Distributed Sens. Networks.

[29]  Paulo Leitão,et al.  Agent-based distributed manufacturing control: A state-of-the-art survey , 2009, Eng. Appl. Artif. Intell..

[30]  S S Stevens,et al.  On the Theory of Scales of Measurement. , 1946, Science.

[31]  Klaus Moessner,et al.  Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey , 2017 .

[32]  Jay Lee,et al.  Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation , 2015 .

[33]  Douglas H. Norrie,et al.  Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey , 1999, Knowledge and Information Systems.

[34]  Roberto Teti,et al.  Intelligent Computing Methods for Manufacturing Systems , 1997 .

[35]  Edward H. Glaessgen,et al.  The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .

[36]  Hiesik Kim,et al.  Environmental Monitoring Systems: A Review , 2013, IEEE Sensors Journal.

[37]  Mats Björkman,et al.  Transitioning From Standard Automation Solutions to Cyber-Physical Production Systems: An Assessment of Critical Conceptual and Technical Challenges , 2018, IEEE Systems Journal.

[38]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[39]  Xi Vincent Wang,et al.  A cloud-based production system for information and service integration: an internet of things case study on waste electronics , 2017, Enterp. Inf. Syst..

[40]  Jiafu Wan,et al.  Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications , 2017, IEEE Access.

[41]  Fei Tao,et al.  Digital Twin Service towards Smart Manufacturing , 2018 .

[42]  Yin Zhang,et al.  GroRec: A Group-Centric Intelligent Recommender System Integrating Social, Mobile and Big Data Technologies , 2016, IEEE Transactions on Services Computing.

[43]  Kalyanmoy Deb,et al.  Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications , 2017, Expert Syst. Appl..

[44]  Amit P. Sheth,et al.  Physical-Cyber-Social Computing: Looking Back, Looking Forward , 2015, IEEE Internet Comput..

[45]  Kalyanmoy Deb,et al.  Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey , 2017, Expert Syst. Appl..

[46]  Arquimedes Canedo,et al.  Industrial IoT lifecycle via digital twins , 2016, 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[47]  Athanasios V. Vasilakos,et al.  A Manufacturing Big Data Solution for Active Preventive Maintenance , 2017, IEEE Transactions on Industrial Informatics.

[48]  Ying Liu,et al.  A categorical framework of manufacturing for industry 4.0 and beyond , 2016 .

[49]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[50]  Taekyoung Kwon,et al.  An Experimental Study of Hierarchical Intrusion Detection for Wireless Industrial Sensor Networks , 2010, IEEE Transactions on Industrial Informatics.

[51]  Dimitris Mourtzis,et al.  Industrial Big Data as a Result of IoT Adoption in Manufacturing , 2016 .

[52]  Nezih Mrad,et al.  The role of data fusion in predictive maintenance using digital twin , 2018 .

[53]  L. Pietre-Cambacedes,et al.  Cryptographic Key Management for SCADA Systems-Issues and Perspectives , 2008, 2008 International Conference on Information Security and Assurance (isa 2008).

[54]  Sandro Wartzack,et al.  Shaping the digital twin for design and production engineering , 2017 .

[55]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[56]  Noël Crespi,et al.  Dynamic Social Structure of Things: A Contextual Approach in CPSS , 2015, IEEE Internet Computing.

[57]  Andrew McAfee,et al.  Enterprise 2.0: the dawn of emergent collaboration , 2006, IEEE Engineering Management Review.

[58]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .

[59]  Victor C. M. Leung,et al.  A Novel Sensory Data Processing Framework to Integrate Sensor Networks With Mobile Cloud , 2016, IEEE Systems Journal.

[60]  S. Michael Spottswood,et al.  Reengineering Aircraft Structural Life Prediction Using a Digital Twin , 2011 .

[61]  Peter Kopacek,et al.  Intelligent Manufacturing:Present State and Future Trends , 1999, J. Intell. Robotic Syst..