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Gate Road Support Deformation Forecasting Based on Multivariate Singular Spectrum Analysis and Fuzzy Time Series

Author

Listed:
  • Luka Crnogorac

    (Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia)

  • Rade Tokalić

    (Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia)

  • Zoran Gligorić

    (Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia)

  • Aleksandar Milutinović

    (Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia)

  • Suzana Lutovac

    (Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia)

  • Aleksandar Ganić

    (Faculty of Mining and Geology, University of Belgrade, Đušina 7, 11000 Belgrade, Serbia)

Abstract

Underground mining engineers and planners in our country are faced with extremely difficult working conditions and a continuous shortage of money. Production disruptions are frequent and can sometimes last more than a week. During this time, gate road support is additionally exposed to rock stress and the result is its progressive deformation and the loss of functionality of gate roads. In such an environment, it is necessary to develop a low-cost methodology to maintain a gate road support system. For this purpose, we have developed a model consisting of two main phases. The first phase is related to support deformation monitoring, while the second phase is related to data analysis. To record support deformations over a defined time horizon we use laser scanning technology together with multivariate singular spectrum analysis to conduct data processing and forecasting. Fuzzy time series is applied to classify the intensity of displacements into several independent groups (clusters).

Suggested Citation

  • Luka Crnogorac & Rade Tokalić & Zoran Gligorić & Aleksandar Milutinović & Suzana Lutovac & Aleksandar Ganić, 2021. "Gate Road Support Deformation Forecasting Based on Multivariate Singular Spectrum Analysis and Fuzzy Time Series," Energies, MDPI, vol. 14(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3710-:d:579150
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    References listed on IDEAS

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    1. Mahdi Kalantari & Hossein Hassani, 2019. "Automatic Grouping in Singular Spectrum Analysis," Forecasting, MDPI, vol. 1(1), pages 1-16, October.
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