It is well known that mechanical systems require supervision and maintenance procedures. There are a lot of condition monitoring techniques that are commonly used, and in the era of IoT and predictive maintenance one may find plenty of solutions for various applications. Unfortunately in the case of belt conveyors used in underground mining a list of possible solutions shrinks quickly. The reason is that they are specific mechanical systems—the typical conveyor is located in the mining tunnel and its length may vary between 100 and 1000 m. According to mining regulations, visual inspection of the conveyor route should be done before it will start the operation. On the other hand, since environmental conditions in mining tunnels are extremely harsh and the risk of accidents is high, there is a tendency to minimize human presence in the tunnels. In this paper, we propose a prototype of an inspection robot based on a UGV platform that could support maintenance staff during the inspection. At present, the robot is controlled by an operator using radio however, we plan to make it autonomous. Moreover, its support could be significant—the robot can “see” elements of the conveyor route (RGB camera) and can identify hot spots using infrared thermography. Moreover, the detected hot spots could be localized and its position can be stored together with both types of images. In parallel, it is possible to preview images in a real-time and stored data allow analysing state of conveyor system after the inspection mission. It is also important that due to radio control systems, an operator can stay in a safe place. Such a robot can be classified as a mobile monitoring system for spatially distributed underground infrastructure.

Air-quality measurements in a deep underground mine are a critical issue. The cost of ventilation, as well as the geometry of the considered mine, make this process very difficult, and local air quality may be a danger to miners. Thus, portable, personal devices are required to inform miners about gas hazards. There are available tools for that purpose; however, they do not allow the storage of data collected during a shift. Moreover, they do not allow the basic analysis of the acquired data cost-effectively. This paper aims to present a system using low-cost gas sensors and microcontrollers, and takes advantage of commonly used smartphones as a computing and visualization resource. Finally, we demonstrate monitoring system results from a test in an underground mine located in Poland.

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.