The DigitalTwin from an Artificial Intelligence Perspective

Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical. To reduce modeling efforts and to synchronize results, for each system, a common and unique virtual representation used by all services during the whole system life-cycle is needed, i.e. a DigitalTwin. In this paper such a DigitalTwin, namely the AI reference model AITwin, is defined. This reference model is verified by using a running example from process industry and by analyzing the work done in recent projects.

[1]  Oliver Niggemann,et al.  Automated process planning for cyber-physical production systems , 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

[2]  Eleonora Bottani,et al.  Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants , 2020 .

[3]  Abdulmotaleb El Saddik,et al.  C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems , 2017, IEEE Access.

[4]  Guillaume Lample,et al.  Deep Learning for Symbolic Mathematics , 2019, ICLR.

[5]  Bo Wang,et al.  Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry , 2019, Int. J. Inf. Manag..

[6]  Andrew Y. C. Nee,et al.  Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison , 2019, Engineering.

[7]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[8]  Yongli Wei,et al.  Digital twin for CNC machine tool: modeling and using strategy , 2018, Journal of Ambient Intelligence and Humanized Computing.

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

[10]  Azad M. Madni,et al.  Leveraging Digital Twin Technology in Model-Based Systems Engineering , 2019, Syst..

[11]  Haiwen Zhu,et al.  Improved Data Mining for Production Diagnosis of Gas Wells with Plunger Lift through Dynamic Simulations , 2019, Day 2 Tue, October 01, 2019.

[12]  Christopher M. Bishop,et al.  Bayesian Neural Networks , 1997, J. Braz. Comput. Soc..

[13]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[14]  Omer San,et al.  Digital Twin: Values, Challenges and Enablers From a Modeling Perspective , 2019, IEEE Access.

[15]  Edward A. Lee.  CPS foundations , 2010, Design Automation Conference.

[16]  Oliver Niggemann,et al.  Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder , 2019, 2019 IEEE International Conference on Industrial Technology (ICIT).

[17]  Connor Jennings,et al.  A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests , 2017 .

[18]  Oliver Niggemann,et al.  Non-convex hull based anomaly detection in CPPS , 2020, Eng. Appl. Artif. Intell..

[19]  Oliver Niggemann,et al.  Design of industrial automation systems — Formal requirements in the engineering process , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).

[20]  Kai-Uwe Kühnberger,et al.  Neural-Symbolic Learning and Reasoning: A Survey and Interpretation , 2017, Neuro-Symbolic Artificial Intelligence.

[21]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[22]  Oliver Niggemann,et al.  Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory , 2019, AAAI.

[23]  Vittaldas V. Prabhu,et al.  A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis , 2017, APMS.

[24]  Robert X. Gao,et al.  Machine learning-based image processing for on-line defect recognition in additive manufacturing , 2019, CIRP Annals.

[25]  Oliver Niggemann,et al.  LSTM for Model-based Anomaly Detection in Cyber-Physical Systems , 2020, DX.

[26]  Enzo Morosini Frazzon,et al.  A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing , 2019, Int. J. Inf. Manag..

[27]  Zhixiong Li,et al.  Prediction of surface roughness in extrusion-based additive manufacturing with machine learning , 2019, Robotics and Computer-Integrated Manufacturing.