Recently, there has been a growing interest in the mining industry in issues related to risk assessment and management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies have indicated that risk in mining should be analyzed not only in the human factor aspect, but also in strategic (environmental impact) and operational ones. However, there is a lack of research on systematic literature reviews and surveys of studies that would focus on these identified risk aspects simultaneously. Therefore, the purpose of this article is to develop a literature review in the area of analysis, assessment and risk management in the mining sector, published in the last decade and based on the concept of a human engineering system. Following this, a systematic search was performed with the use of Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria were: (a) not older than 10 years, (b) article written in English, (c) publication type (scientific article, book, book chapter), (d) published in chosen electronic collections (Springer, Taylor and Francis, Elsevier, Science Direct, JSTOR). This resulted in the selection of the 94 most relevant papers in the area. First, the general bibliometric analysis was conducted. Later, the selected papers in this review were categorized into four groups and the critical review was developed. One of the main advantages of this study is that the results are obtained from different scientific sources/databases thanks to using a multi-search tool. Moreover, the authors identified the main research gaps in the area of the implementation of risk management in the mining industry.

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

The rising automation level and development of the Industry 4.0 concept in the mining sector increase the risk of cyber-attacks. As a result, this article focuses on developing a risk analysis method that integrates Kaplan’s and Garrick’s approach and fuzzy theory. The proposed approach takes into account the level of automation of the operating mining processes. Moreover, it follows five main steps, including identifying the automation level in a selected mine, definition of cyber-attack targets, identification of cyber-attack techniques, definition of cyber-attack consequences, and risk ratio assessment. The proposed risk assessment procedure was performed according to three cyber-attack targets (databases, internal networks, machinery) and seven selected types of cyber-attack techniques. The fuzzy theory is implemented in risk parameter estimation for cyber-attack scenario occurrence in the mining industry. To illustrate the given method’s applicability, seven scenarios for three levels of mine automation are analyzed. The proposed method may be used to reveal the current cybersecurity status of the mine. Moreover, it will be a valuable guide for mines in which automation is planned in the near future.

In underground mining, new workings (tunnels) are constructed by blasting or mechanical excavation. The blasting technique used in underground mines is supported by economic aspects, especially for deposits characterized by hard rocks. Unfortunately, the quality of the result may be different than expected in terms of the general geometry of work or the roughness of excavation surfaces. The blasting technique is also a source of vibrations that may affect other existing structures, affecting their stability. Therefore, it is of great importance to monitor both the quality of the new tunnels and changes in existing tunnels that may cause rockfall from the sidewalls and ceilings of both new and existing tunnels. The length of mining tunnels and support structures in underground mines is massive. Even if one would like to limit monitoring of tunnel geometry to those used every day for major technological processes such as transport, it is a vast amount of work. What is more, any stationary monitoring system is hard to utilize both due to everyday blasting procedures and mobile machine operation. The method proposed here is based on quick LiDAR/Terrestrial Laser Scanner measurements to obtain a cloud of points, which allows generating the spatial model of a mine’s geometry. Data processing procedures are proposed to extract several parameters describing the geometry of the tunnels. Firstly, the model is re-sampled to obtain its uniform structure. Next, a segmentation technique is applied to separate the cross sections with a specific resolution. Statistical parameters are selected to describe each cross section for final 1D feature analysis along the tunnel length. Such a set of parameters may serve as a basis for blasting evaluation, as well as long-term deformation monitoring. The methodology was tested and validated for the data obtained in a former gold and arsenic mine Zloty Stok, Poland.

Standard hydraulic breaking hammers are widely used for crushing oversized blasted materials and concrete structures demolition in industry. These hammers, installed in on-surface working excavators or stationary manipulators at the dumping points of underground conveyors, provide the required limited sizes of bulk materials and enable the safe operation of other equipment (screens, crushers). In parallel, hydraulic hammers have an alternative—fully electric hammers. This paper aims to review existing linear electric motor (LEM) hammers as an energy-saving and eco-friendly solution in industry. Global market analysis is presented with potential branches of LEM hammers. Several aspects for implementation—design optimization, dynamics simulation, machine control, and performance estimation—are considered. Different case studies for LEM-hammer application are given. The preliminary measurements are demonstrated on the electric hammer of Lekatech Company, which is intended for the mining industry and construction demolition. Experiments showed that depending on the impact frequency, type of rock, and shape of the crushing tool, the time to fracture varies significantly. Optimal parameters exist for every case, for which adjusting requires online hammer control.

Belt mistracking is one of the most dangerous and costly failures of belt conveyors. This phenomenon can lead to complete damage to the conveyor belt, increased demand for electricity from the drive, accelerated wear of route elements, and poses a fire hazard. The article presents the most important causes and ways to detect belt mistracking. Based on the tests carried out at the laboratory test rig, the mechanical and operating parameters of the electric drive of conveyor operation burdened with the problem of lateral running off for the tested belt were compared. The mistracking causes a sudden, differentiated increase in the resistance to movement of the machine, which directly affects the recorded values of forces in the drive system and tensioning mechanism and increases the electrical energy consumption of the drive motor. Belt run monioring based on these parameters is not precise, because there are many other factors on the conveyor route, potentially responsible for the increase in recorded currents and forces. The paper presents an analysis of the possibility of using three, non-contact methods directly monitoring the lateral run-off of the belt, based on vibration measurements of conveyor pulley bearing, RGB images, thermal imaging and measuring the compressive force on the tensioning device.