Growing demand for raw materials forces mining companies to reach deeper deposits. Difficult environmental conditions, especially high temperature and the presence of toxic/explosives gases, as well as high seismic activity in deeply located areas, pose serious threats to humans. In such conditions, running an exploration strategy of machinery parks becomes a difficult challenge, especially from the point of view of technical facilities inspections performed by mining staff. Therefore, there is a growing need for new, reliable, and autonomous inspection solutions for mining infrastructure, which will limit the role of people in these areas. In this article, a method for detection of conveyor rollers failure based on an acoustic signal is described. The data were collected using an ANYmal autonomous legged robot inspecting conveyors operating at the Polish Ore Enrichment Plant of KGHM Polska Miedź S.A., a global producer of copper and silver. As a part of an experiment, about 100 m of operating belt conveyor were inspected. The sound-based fault detection in the plant conditions is not a trivial task, given a considerable level of sonic disturbance produced by a plurality of sources. Additionally, some disturbances partially coincide with the studied phenomenon. Therefore, a suitable filtering method was proposed. Developed diagnostic algorithms, as well as ANYmal robot inspection functionalities and resistance to underground conditions, are developed as a part of the “THING–subTerranean Haptic INvestiGator” project.

Conveying systems are responsible for a large part of continuous horizontal transportation in underground mines. The total length of a conveyor network can reach hundreds of kilometers, while a single conveyor usually has a route length of about 0.5–2 km. The belt is a critical and one of the most costly components of the conveyor, and damage to it can result in long unexpected stoppages of production. This is why proper monitoring of conveyor belts is crucial for continuous operation. In this article, algorithms for the detection of potential damage to a conveyor belt are described. The algorithms for analysis used video recordings of a moving belt conveyor, which, in case the of hazardous conditions of deep mines, can be collected, for example, by a legged autonomous inspection robot. The video was then analyzed frame by frame. In this article, algorithms for edge damage detection, belt deviation, and conveyor load estimation are described. The main goal of the research was to find a potential application for image recognition to detect damage to conveyor belts in mines.

The main task of mineral processing plants is to further process the raw material extracted in the mining faces into a concentrate with the highest possible concentration of the final product. In practice, it is a complex process in which several stages can be distinguished. After the ore has been transported to the surface by the skip shaft, one of the first steps is sieving the ore, which is typically performed using vibrating mining screens. In a typical Ore Enrichment Plant, the screening process is carried out by several such machines. This is a typical bottleneck in the technological chain. For this reason, the main challenge for users is to achieve the highest reliability and efficiency of these technical facilities. The solution is to focus on predictive maintenance strategies based on the development of monitoring and advanced diagnostic procedures capable of estimating the time of safe operation. This work was developed as part of an advanced diagnostic system ensuring comprehensive technical conditioning and early fault detection of components such as the engine, transmission, bearings, springs, and screen. This article focuses on vibration data. The problem of damage detection in the presence of periodically impulsive components resulting from falling feed material on the screen and its further screening process has been considered. These disturbances are of a non-Gaussian noise nature, the elimination of which is essential to extract the fault-related signal of interest. One solution may be to properly smooth and filter the raw signal. In this article, a wavelet filtering technique is applied. First, the wavelet filtering procedure is described. In the next step, the performance of a wavelet filter is investigated depending on its parameters. Then, the results of wavelet filtering are compared with such methods as low-pass filtering and smoothing using a moving average. Finally, the impact of wavelet filtering on the calculation of screen trajectories is investigated.

We study a critical behavior of mixtures of two species of ultracold bosons trapped in an optical lattice. Using mean-field approximation, we determine the ground-state phase diagram of the system for a wide range of parameters. The introduction of interactions between different species of atoms strongly renormalizes the phase diagram. It can alter the critical behavior modifying multicriticality of crossing points and order of phase transitions in their vicinity between mixed and superfluid states. For selected values of model parameters, the behavior of the system falls out of the $XY$ model universality class, which usually is a hallmark of superfluid phase transition. We supplement our analysis with analytical calculations to explain the observed scenario.

Automated detection of work cycles in underground ore haulage is crucial for optimizing operational efficiency and minimizing costs, yet existing methods often rely on manual logging or basic telematics, which can be inefficient and prone to errors. This study addresses this gap by presenting a novel approach that utilizes IMU sensor mounted on haul trucks to accurately estimate ore haulage cycles. By collecting data from various work shifts, we analyzed 1473 unique ore haulage cycles across various machines and environments, demonstrating the robustness of our method. The proposed solution incorporates advanced deep learning models, specifically VGG16 and autoencoders, to process the sensor data and detect cycles effectively. Our approach not only enhances real-time tracking of truck operations but also enables better route planning and load management, thereby reducing idle time. The model achieved an accuracy of approximately 87%, validating its effectiveness as an easy to implement solution for work cycle detection in the challenging conditions of underground mines. This automation facilitates continuous optimization by providing valuable insights into operational performance, contributing to improved long-term productivity. The findings indicate that leveraging IMU sensor data through machine learning techniques presents a significant advancement in the field of ore haulage cycle detection, ultimately benefiting the mining industry by enhancing efficiency and reducing operational costs.