Skip to main content

Measuring Dependencies in Cyber-Physical Systems: Overhead Cranes Case Study

  • Conference paper
  • First Online:
Innovative Intelligent Industrial Production and Logistics (IN4PL 2020, IN4PL 2021)

Abstract

Complex cyber-physical systems with multifunctional duties generally degrade simultaneously due to common factors such as the designed structure, the operating environment, and the exploitation history (we refer to exploitation when we include operation and maintenance processes together). Risk-oriented modeling is a well-established methodology for degradation analysis and prediction, and one of the major challenges is the modelling features of the marginals that describe, and store information related to the degradation process. Usually, the modelling process for each component of the system is analyzed independently, but in recent years the multivariate copulas developed and implemented in formal programming languages allow us to consider both sides of the modelling process in one (independent and dependent components). In this paper, we discuss the impact through sensitivity analysis of the paradigm shift between the assumption of independence or dependencies between components. The paper measures the impact of the decision in a parameterized scenario showing significant changes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Villalonga, A., et al.: A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annu. Rev. Control. 51, 357–373 (2021)

    Article  Google Scholar 

  2. Zhu, W.: A spatial decision-making model of smart transportation and urban planning based on coupling principle and Internet of Things. Comput. Electr. Eng. 102, 108222 (2022)

    Article  Google Scholar 

  3. Jun, L., Jun, W.: Cloud computing based solution to decision making. Procedia Eng. 15, 1822–1826 (2011)

    Article  Google Scholar 

  4. Wong, P.-M., Chui, C.-K.: Cognitive engine for augmented human decision-making in manufacturing process control. J. Manuf. Syst. 65, 115–129 (2022)

    Article  Google Scholar 

  5. Dong, Y., Sun, C., Han, Y., Liu, Q.: Intelligent optimization: A novel framework to automatize multi-objective optimization of building daylighting and energy performances. J. Build. Eng. 43, 102804 (2021)

    Article  Google Scholar 

  6. Zimmermann, E., Mezgebe, T.T., El Haouzi, H.B.R.I.L., Thomas, P., Pannequin, R., Noyel, M.: Multicriteria decision-making method for scheduling problem based on smart batches and their quality prediction capability. Comput. Indust. 133, 103549 (2021)

    Google Scholar 

  7. Jun, C., Lee, J.Y., Kim, B.H., Noh, S.D.: Automatized modeling of a human engineering simulation using Kinect. Robot. Comput.-Integrat. Manufact. 55(Part B), 259–264 (2019)

    Google Scholar 

  8. Szpytko, J., Salgado Duarte, Y.: Integrated maintenance platform for critical cranes under operation: Database for maintenance purposes. In: 4th IFAC Workshop on Advanced Maintenance Engineering, Services, and Technologies, Cambridge (2020)

    Google Scholar 

  9. Szpytko, J., Salgado Duarte, Y.: Exploitation efficiency system of crane based on risk management. In: Proceeding of the International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2020, 2–4 November 2020 (2020)

    Google Scholar 

  10. Sun, F., Fangyou, F., Liao, H., Dan, X.: Analysis of multivariate-dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula. Reliab. Eng. Syst. Saf. 204, 107168 (2020)

    Article  Google Scholar 

  11. Qifa, X., Fan, Z., Jia, W., Jiang, C.: Fault detection of wind turbines via multivariate process monitoring based on vine copulas. Renewab. Energy 161, 939–955 (2020)

    Article  Google Scholar 

  12. Szpytko, J., Salgado Duarte, Y.: Technical devices degradation self-analysis for self-maintenance strategy: Crane case study. In: Proceedings of INCOM 2021, June 2021, 17th IFAC Symposium on Information Control Problems in Manufacturing (2021)

    Google Scholar 

Download references

Acknowledgement

The work has been financially supported by the Polish Ministry of Education and Science.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yorlandys Salgado-Duarte .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Szpytko, J., Salgado-Duarte, Y. (2023). Measuring Dependencies in Cyber-Physical Systems: Overhead Cranes Case Study. In: Smirnov, A., Panetto, H., Madani, K. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL IN4PL 2020 2021. Communications in Computer and Information Science, vol 1855. Springer, Cham. https://doi.org/10.1007/978-3-031-37228-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37228-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37227-8

  • Online ISBN: 978-3-031-37228-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics