Virtual engineering of cyber-physical automation systems: The case of control logic

Abstract Mastering the fusion of information and communication technologies with physical systems to cyber-physical automation systems is of main concern to engineers in the industrial automation domain. The engineering of these systems is challenging as their distributed nature and the heterogeneity of stakeholders and tools involved in their engineering contradict the need for the simultaneous engineering of their cyber and physical parts over their life cycle. This paper presents a novel approach based on the virtual engineering method, which provides support for the simultaneous engineering of the cyber and physical parts of automation systems. The approach extends and integrates the life cycle centered view mandated by current conceptual architectures and the digital twin paradigm with an integrated, iterative engineering method. The benefits of the approach are highlighted in a case study related to the engineering of the control logic of a cyber physical automation system originating from the process engineering domain. We describe for the first time a modular domain ontology, which formally describes the cyber and physical part of the system. We present cyber services built on top of the ontology layer, which allow to automatically verify different control logic types and simultaneously verify cyber and physical parts of the system in an incremental manner.

[1]  Birgit Vogel-Heuser,et al.  Challenges for Software Engineering in Automation , 2014 .

[2]  Wolfgang Marquardt,et al.  OntoCAPE - A large-scale ontology for chemical process engineering , 2007, Eng. Appl. Artif. Intell..

[3]  K. PandeyR. Object constraint language (OCL) , 2011 .

[4]  Anton Strahilov,et al.  Engineering Workflow and Software Tool Chains of Automated Production Systems , 2017, Multi-Disciplinary Engineering for Cyber-Physical Production Systems.

[5]  Pieter Pauwels,et al.  Reusing Domain Ontologies in Linked Building Data: The Case of Building Automation and Control , 2017, JOWO.

[6]  Iwona Grobelna,et al.  Design and Verification of Real-Life Processes With Application of Petri Nets , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  Alexander Fay,et al.  Transformation of GRAFCET to PLC code including hierarchical structures , 2017 .

[8]  Victor Haefner,et al.  PolyVR - A Virtual Reality Authoring System , 2014, EuroVR.

[9]  Christos Pateritsas,et al.  A query language for multi-version data web archives , 2016, Expert Syst. J. Knowl. Eng..

[10]  Helen Gill,et al.  Cyber-Physical Systems , 2019, 2019 IEEE International Conference on Mechatronics (ICM).

[11]  Siddhartha Kumar Khaitan,et al.  Design Techniques and Applications of Cyberphysical Systems: A Survey , 2015, IEEE Systems Journal.

[12]  Marcela Vegetti,et al.  ISA-88 Formalization. A Step Towards its Integration with the ISA-95 Standard , 2014, FOMI@FOIS.

[13]  Olaf Stursberg,et al.  An Experimental Batch Plant as a Test Case for the Verification of Hybrid Systems , 2001, Eur. J. Control.

[14]  Bilal Ahmad,et al.  Engineering Methods and Tools for Cyber–Physical Automation Systems , 2016, Proceedings of the IEEE.

[15]  Edward A. Lee,et al.  Industrial Cyber-Physical Systems - iCyPhy , 2013, CSDM.

[16]  Elisabet Estevez,et al.  Automatic transformation of logic models within engineering of embedded mechatronical units , 2011 .

[17]  Boris Motik,et al.  OWL 2 Web Ontology Language: structural specification and functional-style syntax , 2008 .

[18]  Pieter Pauwels,et al.  Ontology-Based Modeling of Control Logic in Building Automation Systems , 2017, IEEE Transactions on Industrial Informatics.

[19]  Sebastian Rudolph,et al.  Foundations of Semantic Web Technologies , 2009 .

[20]  Edrisi Muñoz,et al.  Towards an ontological infrastructure for chemical batch process management , 2010, Comput. Chem. Eng..

[21]  Hajo Rijgersberg,et al.  How semantics can improve engineering processes: A case of units of measure and quantities , 2011, Adv. Eng. Informatics.

[22]  Oscar Ljungkrantz,et al.  Formal Specification and Verification of Industrial Control Logic Components , 2010, IEEE Transactions on Automation Science and Engineering.

[23]  Hendro Wicaksono,et al.  Energy Efficiency Evaluation in manufacturing through an Ontology-Represented Knowledge Base , 2014, Intell. Syst. Account. Finance Manag..

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

[25]  Bradley R. Schmerl,et al.  View Consistency in Architectures for Cyber-Physical Systems , 2011, 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems.

[26]  Marco Pistore,et al.  NuSMV 2: An OpenSource Tool for Symbolic Model Checking , 2002, CAV.

[27]  Marga Marcos,et al.  A Methodological Approach to Model-Driven Design and Development of Automation Systems , 2018, IEEE Transactions on Automation Science and Engineering.

[28]  Ivan Porres,et al.  Consistency of UML class, object and statechart diagrams using ontology reasoners , 2015, J. Vis. Lang. Comput..

[29]  Armin Haller,et al.  Semantic Sensor Network Ontology , 2017 .

[30]  Nicolas Halbwachs,et al.  Programming and Verifying Real-Time Systems by Means of the Synchronous Data-Flow Language LUSTRE , 1992, IEEE Trans. Software Eng..

[31]  Marco Pistore,et al.  Nusmv version 2: an opensource tool for symbolic model checking , 2002, CAV 2002.

[32]  Stefan Biffl,et al.  Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review , 2017, Open J. Inf. Syst..

[33]  Stefan Boschert,et al.  Digital Twin—The Simulation Aspect , 2016 .

[34]  David Hästbacka,et al.  Semantics enhanced engineering and model reasoning for control application development , 2012, Multimedia Tools and Applications.

[35]  Peter Fritzson,et al.  Principles of Object-Oriented Modeling and Simulation with Modelica 3.3: A Cyber-Physical Approach , 2014 .

[36]  Edward A. Lee Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[37]  Christophe Nicolle,et al.  Know Beyond Seeing: Combining Computer Vision with Semantic Reasoning , 2018, 2018 IEEE 12th International Conference on Semantic Computing (ICSC).

[38]  Christian Huemer,et al.  Rahmenwerk zur modellbasierten horizontalen und vertikalen Integration von Standards für Industrie 4.0 , 2017, Handbuch Industrie 4.0.

[39]  Weixing Zhu,et al.  Ontology-Based Semantic Verification for UML Behavioral Models , 2013, Int. J. Softw. Eng. Knowl. Eng..

[40]  J. G. Ovtcharova VIRTUAL ENGINEERING: PRINCIPLES, METHODS AND APPLICATIONS , 2010 .

[41]  Yuji Naka,et al.  An upper ontology based on ISO 15926 , 2007, Comput. Chem. Eng..

[42]  Birgit Vogel-Heuser,et al.  Evolution of software in automated production systems: Challenges and research directions , 2015, J. Syst. Softw..

[43]  Richard Mordinyi,et al.  Integrating heterogeneous engineering knowledge and tools for efficient industrial simulation model support , 2015, Adv. Eng. Informatics.

[44]  Valeriy Vyatkin,et al.  Software Engineering in Industrial Automation: State-of-the-Art Review , 2013, IEEE Transactions on Industrial Informatics.

[45]  Gabor Karsai,et al.  Toward a Science of Cyber–Physical System Integration , 2012, Proceedings of the IEEE.

[46]  Mark Austin,et al.  An ontological framework for knowledge modeling and decision support in cyber-physical systems , 2016, Adv. Eng. Informatics.

[47]  W. Kastner,et al.  The Evolution of Factory and Building Automation , 2011, IEEE Industrial Electronics Magazine.

[48]  Peter F. Patel-Schneider,et al.  OWL 2 Web Ontology Language , 2009 .

[49]  Stefan Biffl,et al.  Semantic Integration of Software and Systems Engineering Environments , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[50]  Christoph Lange,et al.  Ontologies and languages for representing mathematical knowledge on the Semantic Web , 2013, Semantic Web.

[51]  M. Adamski,et al.  Hierarchical UML activity diagrams into control interpreted petri nets transformation , 2012, Proceedings of the 19th International Conference Mixed Design of Integrated Circuits and Systems - MIXDES 2012.

[52]  Valeriy Vyatkin,et al.  Testing automation systems by means of model checking , 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

[53]  Jivka Ovtcharova,et al.  Innovation braucht Resourceful Humans Aufbruch in eine neue Arbeitskultur durch Virtual Engineering , 2015 .

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

[55]  Edward A. Lee,et al.  A model-based design methodology for cyber-physical systems , 2011, 2011 7th International Wireless Communications and Mobile Computing Conference.

[56]  Mathias Bonduel,et al.  OPM: An ontology for describing properties that evolve over time , 2018, LDAC.

[57]  Edward A. Lee,et al.  Taming heterogeneity - the Ptolemy approach , 2003, Proc. IEEE.

[58]  Gerard J. Holzmann,et al.  The Model Checker SPIN , 1997, IEEE Trans. Software Eng..

[59]  Walter Terkaj,et al.  Knowledge-based Conversion of Finite State Machines in Manufacturing Automation , 2019 .

[60]  Diego Latella,et al.  Automatic Verification of a Behavioural Subset of UML Statechart Diagrams Using the SPIN Model-checker , 1999, Formal Aspects of Computing.

[61]  Mathias Oppelt,et al.  Virtuelle Wasserfahrt eines Batchreaktors , 2017 .

[62]  Stefan Biffl,et al.  Multi-Disciplinary Engineering for Cyber-Physical Production Systems, Data Models and Software Solutions for Handling Complex Engineering Projects , 2017 .

[63]  Martin Otter,et al.  Modeling of an Experimental Batch Plant with Modelica , 2006 .