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Assessment of Production System Stability with the Use of the FMEA Analysis and Simulation Models

  • Anna Burduk
  • Mieczysław JagodzińskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

Abstract

In order to ensure smooth functioning of a production system, the stability of its processes must be guaranteed, while on the other hand it must be possible to make quick decisions encumbered with the lowest possible risk. The risk results from the uncertainty associated with making decisions as to the future, as well as from the fact that the implementation of innovations is one of the factors that disturb the current manner of operation of the enterprise. The stability of a production system is defined as maintaining the steady state by the system for a certain assumed period of time. The paper describes a method for analysing and assessing the stability in production systems. In order to determine the extent of the impact of individual risk factors on the selected area of the production system, the FMEA analysis was used. When determining the values of the parameters needed for calculating the Risk Priority Number (RPN), defuzzified values of appropriate linguistic variables were used. A process of ore transportation process with the use of a belt conveyor was used as an example.

Keywords

Stability Production system Modelling and simulation FMEA analysis 

Notes

Acknowledgements

This work has been partly supported by the Institute of Automatic Control under Grant BK/265/RAU1/2014.

References

  1. 1.
    Bubnicki, Z.: Modern Control Theory. Springer, Berlin (2005)Google Scholar
  2. 2.
    Azadegan, A., Probic, L., Ghazinoory, S., Samouei, P.: Fuzzy logic in manufacturing: a review of literature and a specialized application. Int. J. Prod. Econ. 132(2), 258–270 (2011)CrossRefGoogle Scholar
  3. 3.
    Chrysler Cooperation, Ford Motor Company, General Motors Cooperation, Potential Failure Mode and Effects Analysis (FMEA), First Edition Issued (February 1993)Google Scholar
  4. 4.
    Roux, O., Jamali, M., Kadi, D., Chatelet, E.: Development of simulation and optimization platform to analyse maintenance policies performance for manufacturing systems. Int. J. Comput. Integr. Manuf. 2008(21), 407–414 (2008)CrossRefGoogle Scholar
  5. 5.
    Krenczyk, D., Skolud, B.: Transient states of cyclic production planning and control. Appl. Mech. Mater. 657, 961–965 (2014)CrossRefGoogle Scholar
  6. 6.
    Krenczyk, D., Skolud, B.: Production preparation and order verification systems integration using method based on data transformation and data mapping. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part II. LNCS, vol. 6679, pp. 397–404. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Rojek, I.: Neural networks as performance improvement models in intelligent CAPP systems. Control Cybern. 39(1), 55–68 (2010)Google Scholar
  8. 8.
    Sankar, N., Prabhu, B.: Modified approach for prioritization of failures in a system failure mode and effects analysis. Int. J. Qual. Reliab. Manag. 18(3), 324–336 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Mechanical DepartmentWrocław University of TechnologyWrocławPoland
  2. 2.Institute of Automatic ControlSilesian University of TechnologyGliwicePoland

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