Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.
The monitoring of drilling processes is a well-known topic in the mining industry. It is widely used for rock mass characterization, bit wear monitoring and drilling process assessment. However on-board monitoring systems used for this purpose are installed only on a limited number of machines, and breakdowns are possible. There is a need for a data acquisition system that can be used on different drilling rigs and for an automatic data analysis procedure. In this paper, we focused on the automatic detection of drilling cycles, presenting a simple yet reliable system to be universally installed on drilling rigs. The proposed solution covers hardware and software. It is based on the measurement of electric current and acoustic signals. The signal processing methods include threshold-based segmentation, a short-time envelope spectrum and a spectrum for the representation of results. The results of the research have been verified on a real drilling rig within the testing site of its manufacturer by comparing the results with the data of the on-board monitoring system installed on the machine. Novel aspects of our approach include the detection of the pre-boring stage, which has an intermediate amplitude that masks the real drilling cycles, and the use of the percussion instantaneous frequency, which is estimated by acoustic recordings.
Mining has been embedded in the public consciousness as a dirty, non-innovative, outdated and environmentally harmful industry. Proper education, especially the academic one, becomes crucial to successfully change this image. This article depicts the initial assumptions of the international project Master in Entrepreneurship, Innovation and Technology Integration in Mining (MEITIM), co-financed by EIT Raw Materials, that aims to diagnose the current state of Polish higher education in the field of mining at, among others, the Wroclaw University of Science and Technology (WUST), concerning the experience of the Technical University of Madrid (UPM) and Lappeenranta University of Technology (LUT). The MEITIM project allowed identifying the directions of necessary changes in the didactic process as an indispensable set of skills and knowledge for a future mining graduate. Such activities are dictated by many guidelines and arrangements, among others, at the level of the European Commission or key industry institutions such as the International Council on Mining and Metals. These are key competencies that require significant changes in university curricula supporting sustainable development goals in innovative mining. The authors show that there is a link between the condition of the mining industry in Poland, its reputation, and the number of people who want to study mining.
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