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Comparison between Simulation and Analytical Methods in Reliability Data Analysis: A Case Study on Face Drilling Rigs

Author

Listed:
  • Seyed Hadi Hoseinie

    (Department of Mining Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran)

  • Hussan Al-Chalabi

    (Division of Operation & Maintenance Engineering, Lulea University of Technology, 97187 Lulea, Sweden)

  • Behzad Ghodrati

    (Division of Operation & Maintenance Engineering, Lulea University of Technology, 97187 Lulea, Sweden)

Abstract

Collecting the failure data and reliability analysis in an underground mining operation is challenging due to the harsh environment and high level of production pressure. Therefore, achieving an accurate, fast, and applicable analysis in a fleet of underground equipment is usually difficult and time consuming. This paper aims to discuss the main reliability analysis challenges in mining machinery by comparing three main approaches: two analytical methods (white-box and black-box modeling), and a simulation approach. For this purpose, the maintenance data from a fleet of face drilling rigs in a Swedish underground metal mine were extracted by the MAXIMO system over a period of two years and were applied for analysis. The investigations reveal that the performance of these approaches in ranking and the reliability of the studies of the machines is different. However, all mentioned methods provide similar outputs but, in general, the simulation estimates the reliability of the studied machines at a higher level. The simulation and white-box method sometimes provide exactly the same results, which are caused by their similar structure of analysis. On average, 9% of the data are missed in the white-box analysis due to a lack of sufficient data in some of the subsystems of the studies’ rigs.

Suggested Citation

  • Seyed Hadi Hoseinie & Hussan Al-Chalabi & Behzad Ghodrati, 2018. "Comparison between Simulation and Analytical Methods in Reliability Data Analysis: A Case Study on Face Drilling Rigs," Data, MDPI, vol. 3(2), pages 1-12, April.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:2:p:12-:d:140350
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    References listed on IDEAS

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    1. Louit, D.M. & Pascual, R. & Jardine, A.K.S., 2009. "A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1618-1628.
    2. Balbir S. Dhillon, 2008. "Mining Equipment Reliability, Maintainability, and Safety," Springer Series in Reliability Engineering, Springer, number 978-1-84800-288-3, June.
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    Cited by:

    1. Simone Fiori & Andrea Vitali, 2019. "Statistical Modeling of Trivariate Static Systems: Isotonic Models," Data, MDPI, vol. 4(1), pages 1-29, January.
    2. Yuriy Zaporozhets & Artem Ivanov & Yuriy Kondratenko, 2019. "Geometrical Platform of Big Database Computing for Modeling of Complex Physical Phenomena in Electric Current Treatment of Liquid Metals," Data, MDPI, vol. 4(4), pages 1-18, October.

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