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.

This article presents the concept of using the DTW algorithm to partially solve the problem of locating LHD (load, haul, dump) in an underground mine. The concept assumes the recognition of characteristics—patterns that are hidden in vibrations recorded by vehicles—in segments of the route in the underground excavation, which under appropriate conditions enables the obtainment of information similar to that obtained through the use of RFID gates. With the use of this solution in practice, there are several problems that are addressed in this article. One of the main issues is the different arrangement of the signal fragments resulting from driving along with characteristic parts of the route (bumps, paving damage, lumps of excavated material, etc.) at different driving speeds. This problem was solved by using a combination of the road quality detection algorithm and the DTW algorithm, which estimates the similarity of time series with different lengths. The concept was developed and pre-tested using a test rig and a constructed wheeled robot, and then validated in the conditions of the KGHM underground copper mine in Poland, where the readings from the typical haulage process of an LHD vehicle were analyzed.

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.

Self-propelled machines are the main resources used by the Polish copper ore mining industry to transport ore from the mining area to reloading points for conveyor transport. Due to the difficult mining conditions, they must meet high requirements in terms of operational efficiency, safety, and reliability. One of the most significant challenges is high robustness to dynamic overloads. In practice, they have a strong dependency on pavement influence during machine movement, the type of operation, and the driving style of the operator. In this research, we focus on the multivariate analysis of dynamic overloads observed on a large population of haul trucks operating in different mining areas. The main aim of this study was the identification of major factors of excessive dynamic overload that result in damage to structural nodes of machines. In the case of haul track, the joint is such a critical component, that in extreme situations, it breaks and splits the machine in two. There are proposed methods for assessing the occurrence of dynamic overload based on recognized mining conditions and operator behavior. In addition, we propose a method to specify which factors are more meaningful for dynamic overloads. A measurement campaign has been conducted using a mobile inertial sensor interconnected with a developing IoT platform for predictive maintenance of mining infrastructure.