IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7590-d1140034.html
   My bibliography  Save this article

Some Stochastic Aspects of Safety Work of Steel Wire Ropes Used in Mining-Shaft Hoists

Author

Listed:
  • Andrzej Tytko

    (Department of Machinery Engineering and Transport, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

  • Grzegorz Olszyna

    (Department of Machinery Engineering and Transport, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

  • Grzegorz Kocór

    (Team of Appraisers AUTORYTET in Polkowice, 59-100 Polkowice, Poland)

  • Mariusz Szot

    (Department of Mechanical Research and Materials Engineering, Central Mining Institute in Katowice, 40-166 Katowice, Poland)

Abstract

In most rope transport devices, the used steel wire ropes are replaced with new ones after working for a certain period of time, or after reaching the acceptable level of wear. The operational safety of the entire system depends on the correct diagnosis of the condition of the working wire rope. This is not easy, because the working lives of the ropes are varied, and their wear depends on many factors, including random ones. This article presents three different aspects of the work of steel ropes in mining-shaft hoists. What they have in common is a stochastic approach to interpreting the level of rope wear. The process of progressive wear due to fatigue breaking of wires is presented as the first example. This process is non-linear with a strong upward trend, which, in the final stage, turns into a phase often referred to as “explosive”. The rate at which subsequent wire breaks appear is influenced by numerous random factors, e.g., in the form of different methods and materials from which a given rope is constructed. However, the character of the progressing wear process is most affected by the random distribution of stresses experienced by individual wires and the randomly variable nature of the working environment. The second aspect presented in this article is an attempt to determine the probability of wire breaks of the rope. This was presented on the example of wear of the hoisting rope of a mining-shaft hoist. The last aspect of a stochastic nature, which is discussed in the article, is the issue of separating individual components of this distribution from the multimodal distribution describing the tensile strength of the rope wires, possibly of a normal character. Modern methods of analysis allow such distributions to be assigned to specific structural elements of the wire rope. This gives information about which structural elements of the rope wear faster or unusually and, consequently, determine its strength. This was presented on the basis of the results of strength tests of the wires of the mining-shaft hoist rope, which broke due to excessive corrosion wear of the inner strands. The presented examples explain only a short part of randomness in the description of working ropes, but the intention of the authors is to draw the attention of the personnel responsible for their safe operation to unavoidable random factors.

Suggested Citation

  • Andrzej Tytko & Grzegorz Olszyna & Grzegorz Kocór & Mariusz Szot, 2023. "Some Stochastic Aspects of Safety Work of Steel Wire Ropes Used in Mining-Shaft Hoists," Sustainability, MDPI, vol. 15(9), pages 1-13, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7590-:d:1140034
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7590/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/9/7590/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinhua Mi & Yan-Feng Li & Weiwen Peng & Hong-Zhong Huang, 2018. "Reliability Analysis of Complex Multi-state System with Common Cause Failure Based on DS Evidence Theory and Bayesian Network," Springer Series in Reliability Engineering, in: Anatoly Lisnianski & Ilia Frenkel & Alex Karagrigoriou (ed.), Recent Advances in Multi-state Systems Reliability, pages 19-38, Springer.
    2. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    3. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    4. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    5. Eren Bas & Erol Egrioglu & Ufuk Yolcu, 2021. "Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm," Forecasting, MDPI, vol. 3(4), pages 1-11, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuxin Zhang & Yifei Yang & Xiaosi Li & Zijing Yuan & Yuki Todo & Haichuan Yang, 2023. "A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    2. Simona Mikšíková & David Ulčák & František Kuda, 2022. "Analysis of Malfunctions in Selected Parking Systems in the Czech Republic," Sustainability, MDPI, vol. 14(3), pages 1-10, February.
    3. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    4. Hossein Yousefi & Mohammad Hasan Ghodusinejad & Armin Ghodrati, 2022. "Multi-Criteria Future Energy System Planning and Analysis for Hot Arid Areas of Iran," Energies, MDPI, vol. 15(24), pages 1-25, December.
    5. Dyna Heng & Anna Ivanova & Rodrigo Mariscal & Ms. Uma Ramakrishnan & Joyce Wong, 2016. "Advancing Financial Development in Latin America and the Caribbean," IMF Working Papers 2016/081, International Monetary Fund.
    6. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "The implications of monetary expansion in China for the US dollar," Journal of Asian Economics, Elsevier, vol. 46(C), pages 71-84.
    7. Wang, Rongxi & Li, Yufan & Xu, Jinjin & Wang, Zhen & Gao, Jianmin, 2022. "F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A quantitative measure of fitness for duty and work processes for human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 595-601.
    9. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    10. Guo-hua Ye & Mirxat Alim & Peng Guan & De-sheng Huang & Bao-sen Zhou & Wei Wu, 2021. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-13, March.
    11. Ahmed Belhadjayed & Grégoire Loeper & Frédéric Abergel, 2016. "Forecasting Trends With Asset Prices," Post-Print hal-01512431, HAL.
    12. Karzan Mahdi Ghafour & Abdulqadir Rahomee Ahmed Aljanabi, 2023. "The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective," Operations Management Research, Springer, vol. 16(2), pages 780-793, June.
    13. Fieger, Peter & Rice, John, 2016. "Modelling Chinese Inbound Tourism Arrivals into Christchurch," MPRA Paper 75468, University Library of Munich, Germany.
    14. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
    15. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    16. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    17. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    18. Kosuke Kawakami & Hirokazu Kobayashi & Kazuhide Nakata, 2021. "Seasonal Inventory Management Model for Raw Materials in Steel Industry," Interfaces, INFORMS, vol. 51(4), pages 312-324, July.
    19. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    20. Xianbo Li, 2022. "Sequence Model and Prediction for Sustainable Enrollments in Chinese Universities," Sustainability, MDPI, vol. 15(1), pages 1-25, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7590-:d:1140034. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.