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Procedures for Decision Thresholds Finding in Maintenance Management of Belt Conveyor System – Statistical Modeling of Diagnostic Data

  • Paweł K. StefaniakEmail author
  • Agnieszka Wyłomańska
  • Jakub Obuchowski
  • Radoslaw Zimroz
Part of the Lecture Notes in Production Engineering book series (LNPE)

Abstract

Belt conveyors are a key component in material transportation system in both opencast lignite mining and underground copper mines in Poland. Regardless of the structure of the mine, the problem of maintenance of belt conveyors is important (from the entire mining process point of view) for many reasons, such as: (a) conveyors are spatially distributed over a large area, (b) they create logically structured form of complex and heavy components, (c) they are operating in harsh mining environmental conditions, (d) failure of any belt conveyor might result in downtime of the entire production line or its major part. The paper discusses the issue of maintenance of gearboxes used in the conveyor drive systems. The authors have developed a CMMS-class system using GIS technology to support management of conveyors’ network. Its fundamental role is to make right decisions for the exchange of components of the drive systems or allow them to continue their work. Such defined problem requires determination of complex decision rules and the definition of appropriate thresholds of diagnostic parameters. The article presents the procedures for determining decision thresholds, based on statistical modeling of diagnostic data and multidimensional data clustering. By selection of suitable distribution of the data and appropriate statistical parameters, multidimensional data analysis has been performed to determine threshold values for the effective identification of the condition of machines and their components.

Keywords

belt conveyor maintenance management diagnostics data modeling 

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References

  1. 1.
    Bartelmus, W., Zimroz, R.: A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mechanical Systems and Signal Processing 23, 1528–1534 (2009)CrossRefGoogle Scholar
  2. 2.
    Blazej, R., Zimroz, R., Jurdziak, L., Hardygora, M., Kawalec, W.: Conveyor belt condition evaluation vianondestructive testing techniques Mine planning and equipment selection. In: Drebenstedt, C., Singhal, R. (eds.) Proceedings of the 22nd MPES Conference, Dresden, Germany, October 14-19, vol. 2, pp. 1119–1126. Springer, Cham (2014)Google Scholar
  3. 3.
    Brooks, R., Thorpe, R., Wilson, J.: A new method for defining and managing process alarms and for correcting process operation when an alarm occurs. Journal of Hazardous Materials 115 (2004)Google Scholar
  4. 4.
    Cempel, C.: Limit value in practice of vibration diagnosis. Mechanical Systems and Signal Processing 4/6 (1990)Google Scholar
  5. 5.
    Galar, D., Gustafson, A., Tormos, B., Berges, L.: Maintenance Decision Making based on Different types of Data fusion. Eksploatacja i Niezawodnosc – Maintenance and Reliability 14(2), 135–144 (2012)Google Scholar
  6. 6.
    Gordon, A.D.: Classification. Chapman & Hall, London (1999)zbMATHGoogle Scholar
  7. 7.
    Hartigan, J.A.: Clustering algorithms. John Wiley & Sons, Inc. (1975)Google Scholar
  8. 8.
    Hazewinkel, M.: Pareto distribution. Encyclopedia of Mathematics. Springer (2001)Google Scholar
  9. 9.
    Jablonski, A., Barszcz, T., Bielecka, M., Breuhaus, P.: Modeling of probability distribution functions forautomatic threshold calculation in condition monitoring systems. Measurement 46(1), 727–738 (2013)CrossRefGoogle Scholar
  10. 10.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)zbMATHGoogle Scholar
  11. 11.
    Kacprzak, M., Kulinowski, P., Wedrychowicz, D.: Computerized information system used for management of mining belt conveyors operation. Eksploatacja i Niezawodnosc – Maintenance and Reliability 50(2), 81–93 (2011)Google Scholar
  12. 12.
    Lodewijks, G.: Strategies for Automated Maintenance of Belt Conveyor Systems. BulkSolids Handling 24(1), 16–22 (2004)Google Scholar
  13. 13.
    Sawicki, M., Stefaniak, P.K., Krol, R., Zimroz, R., Hardygora, M.: The integration of thermography data and DiagManager system for diagnostic management of technological system. Transport & Logistics (Belgrade), pp. 263–26 (2012)Google Scholar
  14. 14.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)CrossRefzbMATHGoogle Scholar
  15. 15.
    Stefaniak, K., Zimroz, R., Krol, R., Gorniak-Zimroz, J., Bartelmus, W., Hardygora, M.: Some remarks on using condition monitoring for spatially distributed mechanical system belt conveyor network in underground mine - a case study. In: Fakhfakh, T. (ed.) Proceedings of the Second International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO 2012, pp. 497–507. Springer (2012)Google Scholar
  16. 16.
    Zimroz, R., Krol, R., Hardygora, M., Gorniak-Zimroz, J., Bartelmus, W., Gladysiewicz, L., Biernat, S.: A maintenance strategy for drive units used in belt conveyors network. In: Eskikaya, Ş. (ed.) 22nd World Mining Congress & Expo, Istanbul, September 11-16, vol. 1, pp. 433–440. Aydoğdu Ofset, Ankara (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Paweł K. Stefaniak
    • 1
    Email author
  • Agnieszka Wyłomańska
    • 2
  • Jakub Obuchowski
    • 1
  • Radoslaw Zimroz
    • 1
    • 3
  1. 1.Diagnostics and Vibro-Acoustics Science LaboratoryWroclaw University of TechnologyWroclawPoland
  2. 2.Hugo Steinhaus Center, Institute of Mathematics and Computer ScienceWroclaw University of TechnologyWroclawPoland
  3. 3.System Analysis and Process Management DepartmentKGHM Cuprum Ltd., Research & Development CentreWroclawPoland

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