Production monitoring and machine tracking in underground mines based on a collision avoidance system: A case study

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Authors

  • Artur Skoczylas KGHM Curpum Research and Development Centre Ltd, Wroclaw, Poland
  • Natalia Duda-Mróz KGHM Curpum Research and Development Centre Ltd, Wroclaw, Poland
  • Wioletta Koperska KGHM Curpum Research and Development Centre Ltd, Wroclaw, Poland
  • Paweł Stefaniak KGHM Curpum Research and Development Centre Ltd, Wroclaw, Poland
  • Paweł Śliwiński KGHM Polska Miedź SA, Lubin, Poland

Abstract

In the era of Industry 4.0, one of the key challenges facing underground mines is the real-time tracking of both the production process and machinery movements. Significant emphasis is placed on comprehensive monitoring to achieve situational awareness to ensure informational continuity of operations in dispersed organizations. This knowledge is fundamental for safe and efficient extraction, current production reconciliation, and all operational and planning activities, particularly when considering specialized simulation environments for production optimization. So far, implementations of such solutions on an industrial scale have primarily been encountered in open-pit mines or smaller underground mines. This article presents a solution for machine monitoring and tracking based on data from a collision avoidance system, specifically designed for multi-site underground mining enterprises, where the scale of implementation is incomparably more challenging. This anti-collision system was originally designed for detecting machine-to-machine or machine-to-worker collisions. Consequently, the development of validation algorithms, including error correction and adaptive filtering, was imperative. This also required integration with enterprise resource planning (ERP) systems. Moreover, it was also essential to enhance the system infrastructure with additional sensors to enable the registration of machine localization in specified mining zones (e.g., heavy machinery chamber, mining area, loading and unloading point). As part of this study, several analytical models (enhanced by machine learning techniques) were developed to identify movement patterns and cooperation among wheeled transport machinery, as well as the entire course of ore logistics within the mining area. Finally, the process of implementing the system in the target environment is presented, along with a description of the user interface, which features manager dashboards for production visualization.

Keywords:

Industry 4.0, real-time monitoring, underground mining, load-haul-dump (LHD), machine learning, anti-collision system, production optimization, efficiency analysis

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