Advertisement

Model-Driven Systems Engineering: Principles and Application in the CPPS Domain

  • Luca BerardinelliEmail author
  • Alexandra Mazak
  • Oliver Alt
  • Manuel Wimmer
  • Gerti Kappel
Chapter

Abstract

To engineer large, complex, and interdisciplinary systems, modeling is considered as the universal technique to understand and simplify reality through abstraction, and thus, models are in the center as the most important artifacts throughout interdisciplinary activities within model-driven engineering processes. Model-Driven Systems Engineering (MDSE) is a systems engineering paradigm that promotes the systematic adoption of models throughout the engineering process by identifying and integrating appropriate concepts, languages, techniques, and tools. This chapter discusses current advances as well as challenges towards the adoption of model-driven approaches in cyber-physical production systems (CPPS) engineering. In particular, we discuss how modeling standards, modeling languages, and model transformations are employed to support current systems engineering processes in the CPPS domain, and we show their integration and application based on a case study concerning a lab-sized production system. The major outcome of this case study is the realization of an automated engineering tool chain, including the languages SysML, AML, and PMIF, to perform early design and validation.

Keywords

CPPS case study Cyber-physical production systems Model-driven systems engineering Modeling standards V-Model 

Notes

Acknowledgements

This work has been supported by the Christian Doppler Forschungsgesellschaft and the BMWFW, Austria and by the Austrian Research Promotion Agency (FFG) within the project “InteGra 4.0 - Horizontal and Vertical Interface Integration 4.0”.

References

  1. Abedjan, Z., Golab, L. and Naumann, F.: Profiling relational data: a survey. VLDB J. 24 (4), 557–581 (2015)CrossRefGoogle Scholar
  2. Alt, O.: Modellbasierte Systementwicklung mit SysML. Carl Hanser Verlag, Munich (2012)CrossRefGoogle Scholar
  3. Berardinelli, L., Bernardi, S., Cortellessa, V., Merseguer, J.: UML profiles for non-functional properties at work: analyzing reliability, availability and performance. In: ‘Proceedings of NFPinDSML Workshop @ MoDELS’ (2009)Google Scholar
  4. Berardinelli, L., Biffl, S., Lüder, A., Mätzler, E., Mayerhofer, T., Wimmer, M., Wolny, S.: Cross-disciplinary engineering with AutomationML and SysML. Automatisierungstechnik 64 (4), 253–269 (2016)CrossRefGoogle Scholar
  5. Berardinelli, L., Mätzler, E., Mayerhofer, T., Wimmer, M.: Integrating performance modeling in industrial automation through AutomationML and PMIF. In: Proceedings of the IEEE International Conference on Industrial Informatics (INDIN), pp. 1–6 (2016)Google Scholar
  6. Bernardi, S., Merseguer, J., Petriu, D.C.: A dependability profile within MARTE. Softw. Syst. Model. 10 (3), 313–336 (2011)CrossRefGoogle Scholar
  7. Bezivin, J.: On the unification power of models. Softw. Syst. Model. 4 (2), 171–188 (2005)CrossRefGoogle Scholar
  8. Biffl, S., Lüder, A., Mätzler, E., Schmidt, N., Wimmer, M.: Linking and versioning support for AutomationML: a model-driven engineering perspective. In: Proceedings of 2015 IEEE International Conference on Industrial Informatics (INDIN), pp. 499–506 (2015)Google Scholar
  9. Booch, G., Rumbaugh, J., Jacobson, I.: The Unified Modeling Language User Guide, 2nd edn. Addison-Wesley, Reading, MA (2005)Google Scholar
  10. Brambilla, M., Cabot, J., Wimmer, M.: Model-Driven Software Engineering in Practice. Morgan and Claypool, San Rafael (2012)Google Scholar
  11. Broy, M., Schmidt, A.: Challenges in engineering Cyber-Physical Systems. Computer 47 (2), 70–72 (2014)CrossRefGoogle Scholar
  12. Casale, G., Serazzi, G.: Quantitative system evaluation with Java modeling tools. In: Proceedings of the 2nd ACM/SPEC International Conference on Performance Engineering (ICPE), pp. 449–454 (2011)Google Scholar
  13. Cortellessa, V., Di Marco, A., Inverardi, P.: Model-Based Software Performance Analysis. Springer, Berlin (2011)CrossRefGoogle Scholar
  14. Czarnecki, K., Helsen, S.: Feature-based survey of model transformation approaches. IBM Syst. J. 45 (3), 621–645 (2006)CrossRefGoogle Scholar
  15. Dallmeier, V., Knopp, N., Mallon, C., Fraser, G., Hack, S., Zeller, A.: Automatically generating test cases for specification mining. IEEE Trans. Softw. Eng. 38 (2), 243–257 (2012)CrossRefGoogle Scholar
  16. Equipment Center for Distributed Systems: http://www.iaf-bg.ovgu.de/en/technische_ausstattung_cvs.html (2016). [Online; accessed 30 Oct 2016]
  17. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. American Association for Artificial Intelligence, pp. 1–34. AAAI Press, Menlo Park, CA (1996)Google Scholar
  18. Feldmann, S., Kernschmidt, K., Vogel-Heuser, B.: Combining a SysML-based modeling approach and semantic technologies for analyzing change influences in manufacturing plant models. In: Proceedings of the 47th CIRP Conference on Manufacturing Systems (CMS) (2014)Google Scholar
  19. Fleck, M., Troya, J., Wimmer, M.: Search-based model transformations with MOMoT. In: Proceedings of the 9th International Conference on Theory and Practice of Model Transformations (ICMT), pp. 79–87 (2016)Google Scholar
  20. France, R.B., Rumpe, B.: Model-driven development of complex software: a research roadmap. In: Proceedings of the International Conference on Software Engineering (ICSE), pp. 37–54 (2007)Google Scholar
  21. Friedenthal, S., Moore, A., Steiner, R.: A Practical Guide to SysML: the Systems Modeling Language. Morgan Kaufmann, Amsterdam (2014)Google Scholar
  22. Giles, C.L., Miller, C.B., Chen, D., Chen, H.-H., Sun, G.-Z., Lee, Y.-C.: Learning and extracting finite state automata with second-order recurrent neural networks. Neural Comput. 4 (3), 393–405 (1992)CrossRefGoogle Scholar
  23. Graham, S.L., Kessler, P.B., Mckusick, M.K.: Gprof: a call graph execution profiler. SIGPLAN Not. 17 (6), 120–126 (1982)CrossRefGoogle Scholar
  24. Hegny, I., Wenger, M., Zoitl, A.: IEC 61499 based simulation framework for model-driven production systems development. In: Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8 (2010)Google Scholar
  25. Hutchinson, J., Whittle, J., Rouncefield, M., Kristoffersen, S.: Empirical assessment of MDE in industry. In: Proceedings of the 33rd International Conference on Software Engineering (ICSE), pp. 471–480 (2011)Google Scholar
  26. IEC: IEC 62714 – Engineering data exchange format for use in industrial automation systems engineering – AutomationML. http://www.iec.ch (2014)
  27. ISO/PAS: ISO/PAS 17506:2012 Industrial automation systems and integration – COLLADA digital asset schema specification for 3D visualization of industrial data. http://www.iso.org (2012)
  28. Jetley, R., Nair, A., Chandrasekaran, P., Dubey, A.: Applying software engineering practices for development of industrial automation applications. In: Proceedings of the 11th IEEE International Conference on Industrial Informatics (INDIN), pp. 558–563 (2013)Google Scholar
  29. Kagermann, H., Wahlster, W., Helbig, J.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0 – securing the future of German manufacturing industry. Final Report of the Industrie 4.0 Working Group, Forschungsunion im Stifterverband für die Deutsche Wirtschaft e. V. (2013)Google Scholar
  30. Kernschmidt, K., Vogel-Heuser, B.: An interdisciplinary SysML based modeling approach for analyzing change influences in production plants to support the engineering. In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), pp. 1113–1118 (2013)Google Scholar
  31. Kernschmidt, K., Barbieri, G., Fantuzzi, C., Vogel-Heuser, B.: Possibilities and challenges of an integrated development using a combined SysML-model and corresponding domain specific models. In: Proceedings of the 7th IFAC Conference on Manufacturing Modelling, Management, and Control (MIM), pp. 1465–1470 (2013)Google Scholar
  32. Kühne, T.: Matters of (Meta-)modeling. Softw. Syst. Model. 5 (4), 369–385 (2006)CrossRefGoogle Scholar
  33. Kurtev, I.: State of the art of QVT: A model transformation language standard. In: Proceedings of the International Symposium on Applications of Graph Transformations with Industrial Relevance (AGTIVE), pp. 377–393 (2007)Google Scholar
  34. Kyura, N., Oho, H.: Mechatronics–an industrial perspective. IEEE/ASME Trans. Mechatron. 1 (1), 10–15 (1996)CrossRefGoogle Scholar
  35. Lee, E.A.: Cyber physical systems: design challenges. In: Proceedings of the 11th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369 (2008)Google Scholar
  36. Leemans, M., van der Aalst, W.M.P.: Process mining in software systems: discovering real-life business transactions and process models from distributed systems. In: Proceedings of the 18th International Conference on Model Driven Engineering Languages and Systems (MoDELS), pp. 44–53 (2015)Google Scholar
  37. Lúcio, L., Amrani, M., Dingel, J., Lambers, L., Salay, R., Selim, G. M.K., Syriani, E., Wimmer, M.: Model transformation intents and their properties. Softw. Syst. Model. 15 (3), 647–684 (2016)CrossRefGoogle Scholar
  38. Lüder, A., Schmidt, N., Helgermann, S.: Lossless exchange of graph based structure information of production systems by AutomationML. In: Proceedings of IEEE 18th Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4 (2013)Google Scholar
  39. Lüder, A., Schmidt, N., Rosendahl, R.: Data exchange toward PLC programming and virtual commissioning: is AutomationML an appropriate data exchange format? In: Proceedings of the IEEE 13th International Conference on Industrial Informatics (INDIN), pp. 492–498 (2015)Google Scholar
  40. Maga, C.R., Jazdi, N.: An approach for modeling variants of industrial automation systems. In: Proceedings of the IEEE International Conference on Automation Quality and Testing Robotics (AQTR), pp. 1–6 (2010)Google Scholar
  41. Malavolta, I., Lago, P., Muccini, H., Pelliccione, P., Tang, A.: What industry needs from architectural languages: a survey. IEEE Trans. Softw. Eng. 39 (6), 869–891 (2013)CrossRefGoogle Scholar
  42. Mazak, A., Wimmer, M., Huemer, C., Kappel, G., Kastner, W.: Rahmenwerk zur modellbasierten horizontalen und vertikalen Integration von Standards für Industrie 4.0. In: Vogel-Heuser, B. et al. (eds.) Handbuch Industrie 4.0. Springer, Berlin (2016)Google Scholar
  43. Mens, T., Gorp, P.V.: A taxonomy of model transformation. Electron. Notes Theor. Comput. Sci. 152, 125–142 (2006)CrossRefGoogle Scholar
  44. Meyers, B., Deshayes, R., Lucio, L., Syriani, E., Vangheluwe, H., Wimmer, M.: Promobox: a framework for generating domain-specific property languages. In: Proceedings of the 7th International Conference on Software Language Engineering (SLE), pp. 1–20 (2014)Google Scholar
  45. Object Management Group (OMG): Meta Object Facility (MOF) 2.0 Core Specification. OMG Document ptc/03-10-04 (2003)Google Scholar
  46. Object Management Group (OMG): Object Constraint Language (OCL) Specification. Version 2.2. OMG Document formal/2010-02-01 (2010)Google Scholar
  47. Object Management Group (OMG): Meta Object Facility (MOF) 2.0 Query/View/Transformation (QVT). OMG Document formal/2016-06-03 (2016a)Google Scholar
  48. Object Management Group (OMG): OMG Systems Modeling Language (OMG SysML). http://www.omg.org/spec/SysML/1.4/ (2016b)
  49. Object Management Group (OMG): UML Profile for MARTE. Version 1.1. http://www.omg.org/spec/MARTE/1.1/PDF (2016c)
  50. Papakonstantinou, N., Sierla, S.: Generating an Object Oriented IEC 61131-3 software product line architecture from SysML. In: Proceedings of the IEEE 18th Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8 (2013)Google Scholar
  51. PLCopen: PLCopen. http://www.plcopen.org (2011)
  52. Schleipen, M., Drath, R.: Three-view-concept for modeling process or manufacturing plants with AutomationML. In: Proceedings of the IEEE Conference on Emerging Technologies Factory Automation (ETFA), pp. 1–4 (2009)Google Scholar
  53. Schleipen, M., Drath, R., Sauer, O.: The system-independent data exchange format CAEX for supporting an automatic configuration of a production monitoring and control system. In: Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE), pp. 1786–1791 (2008)Google Scholar
  54. Schleipen, M., Selyansky, E., Henssen, R., Bischoff, T.: Multi-level user and role concept for a secure plug-and-work based on OPC UA and AutomationML. In: Proceedings of the 20th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4 (2015)Google Scholar
  55. Schmidt, D.: Guest Editor’s Introduction: Model-Driven Engineering. Computer 39 (2), 25–31 (2006)CrossRefGoogle Scholar
  56. Schütz, D., Legat, C., Vogel-Heuser, B.: MDE of manufacturing automation software – integrating SysML and standard development tools. In: Proceedings of the 12th IEEE International Conference on Industrial Informatics (INDIN), pp. 267–273 (2014)Google Scholar
  57. Seidl, M., Scholz, M., Huemer, C., Kappel, G.: UML@Classroom. Springer, New York (2012)Google Scholar
  58. Selic, B., Gérard, S.: Modeling and Analysis of Real-Time and Embedded Systems with UML and MARTE: Developing Cyber-Physical Systems. Elsevier, Heidelberg (2013)Google Scholar
  59. Smith, C.U.: Performance Engineering of Software Systems. Addison-Wesley Longman Publishing Co., Inc., Reading, MA (1990)Google Scholar
  60. Smith, C.U., Llado, C.M., Puigjaner, R.: Performance Model Interchange Format (PMIF 2): a comprehensive approach to Queueing Network Model interoperability. Perform. Eval. 67 (7), 548–568 (2010)CrossRefGoogle Scholar
  61. Smith, C.U., Williams, L.G.: A performance model interchange format. J. Syst. Softw. 49 (1), 63–80 (1999)CrossRefGoogle Scholar
  62. Stevens, P.: Bidirectional model transformations in QVT: semantic issues and open questions. Softw. Syst. Model. 9 (1), 7–20 (2010)CrossRefGoogle Scholar
  63. Troya, J., Vallecillo, A.: Specification and simulation of queuing network models using domain-specific languages. Comput. Stand. Interfaces 36 (5), 863–879 (2014)CrossRefGoogle Scholar
  64. Vangheluwe, H., Amaral, V., Giese, H., Broenink, J., Schätz, B., Norta, A., Carreira, P., Lukovic, I., Mayerhofer, T., Wimmer, M., Vallecillo, A.: MPM4CPS: multi-paradigm modelling for Cyber-Physical Systems. In: Proceedings of the Project Showcase @ STAF 2015, pp. 1–10 (2016)Google Scholar
  65. Verein Deutscher Ingenieure (VDI): Design methodology for mechatronic system–VDI 2206 (2004)Google Scholar
  66. Vogel-Heuser, B., Biffl, S.: Cross-discipline modeling and its contribution to automation. Automatisierungstechnik 64 (3), 165–167 (2016)CrossRefGoogle Scholar
  67. Vogel-Heuser, B., Fay, A., Schaefer, I., Tichy, M.: Evolution of software in automated production systems: challenges and research directions. J. Syst. Softw. 110, 54–84 (2015)CrossRefGoogle Scholar
  68. Vogel-Heuser, B., Fuchs, J., Feldmann, S., Legat, C.: Interdisziplinärer Produktlinienansatz zur Steigerung der Wiederverwendung. Automatisierungstechnik 63 (2), 99–110 (2015)CrossRefGoogle Scholar
  69. Vyatkin, V.: Software engineering in industrial automation: state-of-the-art review. IEEE Trans. Ind. Inf. 9 (3), 1234–1249 (2013)CrossRefGoogle Scholar
  70. Weilkiens, T.: Systems Engineering with SysML/UML: Modeling, Analysis, Design. Morgan Kaufmann, Waltham (2011)zbMATHGoogle Scholar
  71. Wirth, N.: What can we do about the unnecessary diversity of notation for syntactic definitions? Commun. ACM 20 (11), 822–823 (1977)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Luca Berardinelli
    • 1
    Email author
  • Alexandra Mazak
    • 1
  • Oliver Alt
    • 2
  • Manuel Wimmer
    • 1
  • Gerti Kappel
    • 1
  1. 1.Business Informatics GroupTechnische Universität WienWienAustria
  2. 2.LieberLieber GmbHViennaAustria

Personalised recommendations