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Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

Author

Listed:
  • Edyta Brzychczy

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Cracow, Poland)

  • Paulina Gackowiec

    (Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Cracow, Poland)

  • Mirko Liebetrau

    (Talpasolutions GmbH, 45327 Essen, Germany)

Abstract

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.

Suggested Citation

  • Edyta Brzychczy & Paulina Gackowiec & Mirko Liebetrau, 2020. "Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case," Resources, MDPI, vol. 9(2), pages 1-17, February.
    • Handle: RePEc:gam:jresou:v:9:y:2020:i:2:p:17-:d:317722
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    References listed on IDEAS

    as
    1. Daniel R. Bongers & Hal Gurgenci, 2008. "Fault Detection and Identification for Longwall Machinery Using SCADA Data," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 25, pages 611-641, Springer.
    2. E. A. Elsayed, 2008. "Reliability Prediction and Accelerated Testing," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 7, pages 155-178, Springer.
    3. Izabela Jonek-Kowalska & Marian Turek, 2017. "Dependence of Total Production Costs on Production and Infrastructure Parameters in the Polish Hard Coal Mining Industry," Energies, MDPI, vol. 10(10), pages 1-22, September.
    4. Qiao, Wanguan & Liu, Quanlong & Li, Xinchun & Luo, Xixi & Wan, YuLong, 2018. "Using data mining techniques to analyze the influencing factor of unsafe behaviors in Chinese underground coal mines," Resources Policy, Elsevier, vol. 59(C), pages 210-216.
    5. Michael zur Muehlen & Robert Shapiro, 2015. "Business Process Analytics," International Handbooks on Information Systems, in: Jan vom Brocke & Michael Rosemann (ed.), Handbook on Business Process Management 2, edition 2, pages 243-263, Springer.
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