Skip to main content

Supervising Industrial Distributed Processes Through Soft Models, Deformation Metrics and Temporal Logic Rules

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1160)

Abstract

Typical control solutions for future industrial systems follows a top-down approach, where processes are completely defined at high-level by prosumers (managers) and, later, the control infrastructure decomposes, transforms and delegates the execution of the different parts and activities making up the process into the existing industrial physical components. This view, although may be adequate for certain scenarios; presents several problems when processes are executed by people (workers) or autonomous devices whose programming already describes and controls the activities they perform. On the one hand, people execute processes in a very variable manner. All these execution ways are valid although they can be very different from the original process definition. On the other hand, industrial autonomous devices cannot be requested to execute activities as desired, and their operations can only be supervised. In this context, new control solutions are needed. Therefore, in this paper it is proposed a new process supervision and control system, focused on industrial processes executed in a distributed manner by people and autonomous devices. The proposed solution includes a soft model for industrial processes, which are latter validated through deformation metrics (instead of traditional rigid indicators). Besides, in order to guarantee the coherence of all executions, temporal logic rules are also integrated to evaluate the development of the different activities. Finally, an experimental validation is also provided to analyze the performance of the proposed solution.

Keywords

  • Intelligent control
  • Real-time control
  • Supervised processes
  • Process models
  • Industrial process
  • Data acquisition

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-45691-7_12
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-45691-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   349.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Bordel, B., de Rivera, D.S., Sánchez-Picot, Á., Robles, T.: Physical processes control in Industry 4.0-based systems: a focus on cyber-physical systems. In: Ubiquitous Computing and Ambient Intelligence, pp. 257–262. Springer, Cham (2016)

    Google Scholar 

  2. Geissdoerfer, M., Savaget, P., Bocken, N.M., Hultink, E.J.: The circular economy–a new sustainability paradigm? J. Clean. Prod. 143, 757–768 (2017)

    CrossRef  Google Scholar 

  3. Bordel, B., Alcarria, R., Robles, T., Martín, D.: Cyber–physical systems: extending pervasive sensing from control theory to the Internet of Things. Pervasive Mobile Comput. 40, 156–184 (2017)

    CrossRef  Google Scholar 

  4. Sánchez, B.B., Alcarria, R., de Rivera, D.S., Sánchez-Picot, A.: Enhancing process control in Industry 4.0 scenarios using cyber-physical systems. JoWUA 7(4), 41–64 (2016)

    Google Scholar 

  5. Alcarria, R., Robles, T., Morales, A., López-de-Ipiña, D., Aguilera, U.: Enabling flexible and continuous capability invocation in mobile prosumer environments. Sensors 12(7), 8930–8954 (2012)

    CrossRef  Google Scholar 

  6. Bordel, B., Alcarria, R., de Rivera, D.S., Robles, T.: Process execution in cyber-physical systems using cloud and cyber-physical internet services. J. Supercomput. 74(8), 4127–4169 (2018)

    CrossRef  Google Scholar 

  7. de Rivera, D.S., Alcarria, R., de Andres, D.M., Bordel, B., Robles, T.: An autonomous information device with e-paper display for personal environments. In: 2016 IEEE International Conference on Consumer Electronics (ICCE), pp. 139–140. IEEE (2016)

    Google Scholar 

  8. Bordel, B., Alcarria, R., Sánchez-de-Rivera, D.: A two-phase algorithm for recognizing human activities in the context of Industry 4.0 and human-driven processes. In: World Conference on Information Systems and Technologies, pp. 175–185. Springer, Cham (2019)

    Google Scholar 

  9. Bordel, B., Alcarria, R., Hernández, M., Robles, T.: People-as-a-Service dilemma: humanizing computing solutions in high-efficiency applications. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 31, no. 1, p. 39 (2019)

    Google Scholar 

  10. Boyer, S.A.: SCADA: supervisory control and data acquisition. International Society of Automation (2009)

    Google Scholar 

  11. Zheng, L., Nakagawa, H.: OPC (OLE for process control) specification and its developments. In Proceedings of the 41st SICE Annual Conference, SICE 2002, vol. 2, pp. 917–920. IEEE (2002)

    Google Scholar 

  12. Ahmed, I., Obermeier, S., Naedele, M., Richard III, G.G.: Scada systems: challenges for forensic investigators. Computer 45(12), 44–51 (2012)

    CrossRef  Google Scholar 

  13. Li, D., Serizawa, Y., Kiuchi, M.: Concept design for a web-based supervisory control and data-acquisition (SCADA) system. In: IEEE/PES Transmission and Distribution Conference and Exhibition, vol. 1, pp. 32–36. IEEE (2002)

    Google Scholar 

  14. Nazir, S., Patel, S., Patel, D.: Assessing and augmenting SCADA cyber security: a survey of techniques. Comput. Secur. 70, 436–454 (2017)

    CrossRef  Google Scholar 

  15. Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H., Stoddart, K.: A review of cyber security risk assessment methods for SCADA systems. Comput. Secur. 56, 1–27 (2016)

    CrossRef  Google Scholar 

  16. Sajid, A., Abbas, H., Saleem, K.: Cloud-assisted IoT-based SCADA systems security: a review of the state of the art and future challenges. IEEE Access 4, 1375–1384 (2016)

    CrossRef  Google Scholar 

  17. Yang, T.C.: Networked control system: a brief survey. IEE Proc.-Control Theory Appl. 153(4), 403–412 (2006)

    CrossRef  Google Scholar 

  18. Walsh, G.C., Ye, H.: Scheduling of networked control systems. IEEE Control Syst. Mag. 21(1), 57–65 (2001)

    CrossRef  Google Scholar 

  19. Chen, T.H., Yeh, M.F.: State feedback control based networked control system design with differential evolution algorithm. Univ. J. Control Autom. 5(1), 12–17 (2017)

    Google Scholar 

  20. Goodwin, G.C., Haimovich, H., Quevedo, D.E., Welsh, J.S.: A moving horizon approach to networked control system design. IEEE Trans. Autom. Control 49(9), 1427–1445 (2004)

    MathSciNet  CrossRef  Google Scholar 

  21. Guerra, T.M., Sala, A., Tanaka, K.: Fuzzy control turns 50: 10 years later. Fuzzy Sets Syst. 281, 168–182 (2015)

    MathSciNet  CrossRef  Google Scholar 

  22. Aslam, M., Khan, N.: A new variable control chart using neutrosophic interval method-an application to automobile industry. J. Intell. Fuzzy Syst. 36(3), 2615–2623 (2019)

    CrossRef  Google Scholar 

  23. Kovacic, Z., Bogdan, S.: Fuzzy Controller Design: Theory and Applications. CRC Press, Boco Raton (2018)

    CrossRef  Google Scholar 

  24. Pan, Y., Yang, G.H.: Event-triggered fuzzy control for nonlinear networked control systems. Fuzzy Sets Syst. 329, 91–107 (2017)

    MathSciNet  CrossRef  Google Scholar 

  25. Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., Lennartson, B.: An event-driven manufacturing information system architecture for Industry 4.0. Int. J. Prod. Res. 55(5), 1297–1311 (2017)

    CrossRef  Google Scholar 

  26. Golob, M., Bratina, B.: Web-based control and process automation education and Industry 4.0. Int. J. Eng. Educ. 34(4), 1199–1211 (2018)

    Google Scholar 

  27. MacDonald, I.L., Zucchini, W.: Hidden Markov models for discrete-valued time series. In: Handbook of Discrete-Valued Time Series, pp. 267–286 (2016)

    Google Scholar 

  28. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, L., Wang, G., Cai, J., Chen, T.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018)

    CrossRef  Google Scholar 

Download references

Acknowledgments

The research leading to these results has received funding from the Ministry of Economy and Competitiveness through SEMOLA (TEC2015-68284-R) project and the European Commission through DEMETER project (DT-ICT-08-2019, project ID: 857202).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Borja Bordel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Bordel, B., Alcarria, R., Robles, T. (2020). Supervising Industrial Distributed Processes Through Soft Models, Deformation Metrics and Temporal Logic Rules. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1160. Springer, Cham. https://doi.org/10.1007/978-3-030-45691-7_12

Download citation