Search Results (38)

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

This study presents a discrete event simulation model to minimize operating costs in quarry mining processes by determining the optimal allocation of backhoes and dump trucks, which are the primary mining equipment. The modeling focuses on four principal vehicle types (24-ton dump truck, 2.0 m³ backhoe, 41-ton dump truck, 4.64 m³ backhoe) commonly deployed in quarry mining. The simulation replicates the sequential mining stages involving soil removal, rock ripping (weathered rock or weathered soil), and blasting operations. This methodology is applied to a case study of mining process planning under resource constraints, incorporating real-world quarry conditions in South Korea. Results demonstrate that optimizing the number of equipment units reduces construction costs and shortens the construction period by decreasing dump truck waiting times. When the number of backhoes is limited to 10 during operations, findings indicate an increase in costs and a gradual decline in net profit. Additionally, the interaction between the 24-ton and 41-ton dump trucks is shown to influence the optimal allocation strategy. The simulation-based optimization executes iterative experiments for each scenario, yielding statistically robust results within a 95% confidence interval, thereby supporting informed decision-making for managers. Full article

This article proposes an innovative energy management strategy for hybrid multi-source dump trucks operating under real slope conditions in mining areas. Although previous studies have addressed the energy management issues of hybrid vehicles, few studies have taken into account complex environmental factors such as slopes under actual working conditions. The article overcomes this limitation by integrating a radial basis function (RBF) neural network to directly and accurately predict future vehicle demand power, thereby optimizing the DP-MPC strategy and improving energy efficiency. The results indicate that, compared with the traditional MPC strategy, the proposed strategy reduces fuel consumption by 3.34% and engine start-stop events by 76.2%. Additionally, when compared with another strategy that uses historical data to predict future speed and slope, calculates the vehicle’s future power demand, and incorporates it into the DP-MPC algorithm, the proposed strategy achieves comparable fuel consumption while also reducing engine start-stop events by 69.7%. Notably, the average calculation time for each step is 43.85 ms, which is substantially less than the sampling time of 1 s. To further confirm the real-time performance of the strategy, a hardware-in-the-loop (HIL) test is conducted. Full article

The implementation of fleet management software in mining operations poses challenges, including high initial costs and the need for skilled personnel. Additionally, integrating new software with existing systems can be complex, requiring significant time and resources. This study aims to mitigate these challenges by leveraging advanced technologies to reduce initial costs and minimize reliance on highly trained employees. Through the integration of computer vision and multi-objective optimization, it seeks to enhance operational efficiency and optimize fleet management in open-pit mining. The objective is to optimize truck-to-excavator assignments, thereby reducing excavator idle time and deviations from production targets. A YOLO v8 model, trained on six hours of mine video footage, identifies vehicles at excavators and dump sites for real-time monitoring. Extracted data—including truck assignments and excavator ready times—is incorporated into a multi-objective binary integer programming model that aims to minimize excavator waiting times and discrepancies in target truck assignments. The epsilon-constraint method generates a Pareto frontier, illustrating trade-offs between these objectives. Integrating real-time image analysis with optimization significantly improves operational efficiency, enabling adaptive truck-excavator allocation. This study highlights the potential of advanced computer vision and optimization techniques to enhance fleet management in mining, leading to more cost-effective and data-driven decision-making. Full article

The motor drive system is pivotal for vehicles, particularly in new energy applications. However, conventional hybrid systems, which combine generator sets and single batteries in parallel configurations, fail to meet the operational demands of large pure electric mining dump trucks under fluctuating power requirements—such as high reserve power during acceleration and robust energy recovery during braking. Traditional single-motor configurations struggle to balance low-speed, high-torque operations and high-speed driving within cost-effective ranges, often necessitating oversized motors or multi-gear transmissions. To address these challenges, this paper proposes a dual-motor symmetric powertrain configuration with a seven-speed gearbox, tailored to the extreme operating conditions of mining environments. By integrating a high-speed, low-torque motor and a low-speed, high-torque motor through dynamic power coupling, the system optimizes energy utilization while ensuring sufficient driving force. The simulation results under extreme conditions (e.g., 33% gradient climbs and heavy-load downhill braking) demonstrate that the proposed configuration achieves a peak torque of 267 kNm (200% improvement over single-motor systems) and a system efficiency of 92.4% (vs. 41.7% for diesel counterparts). Additionally, energy recovery efficiency reaches 85%, reducing energy consumption to 4.75 kWh/km (83% lower than diesel trucks) and life cycle costs by 38% (USD 5.34/km). Field tests in open-pit mines validate the reliability of the design, with less than a 1.5% deviation in simulated versus actual performance. The modular architecture supports scalability for 60–400-ton mining trucks, offering a replicable solution for zero-emission mining operations in high-altitude regions, such as Tibet’s lithium mines, and advancing global efforts toward carbon neutrality. Full article

Open-pit mining involves the use of vehicles with high load capacity and satisfactory mobility. As experience shows, these requirements are fully met by pneumatic wheeled dump trucks, the traction drives of which can be made using thermal or electric machines. The latter are preferable due to their environmental friendliness. Unlike dump trucks with thermal engines, which require fuel to be injected into them, electric trucks can be powered by various options of a power supply: centralized, autonomous, and combined. This paper highlights the advantages and disadvantages of different power supply systems depending on their schematic solutions and the quarry parameters for all the variants of the power supply of the dumper. Each quantitative indicator of each factor was changed under conditions consistent with the others. The steepness of the road elevation in the quarry and its length were the factors under study. The studies conducted show that the energy consumption for dump truck movement for all variants of a power supply practically does not change. Another group of factors consisted of electric energy sources, which were accumulator batteries and double electric layer capacitors. The analysis of energy efficiency and the regenerative braking system reveals low efficiency of regeneration when lifting the load from the quarry. In the process of lifting from the lower horizons of the quarry to the dump and back, kinetic energy is converted into heat, reducing the efficiency of regeneration considering the technological cycle of works. Taking these circumstances into account, removing the regenerative braking systems of open-pit electric dump trucks hauling soil or solid minerals from an open pit upwards seems to be economically feasible. Eliminating the regenerative braking system will simplify the design, reduce the cost of a dump truck, and free up usable volume effectively utilized to increase the capacity of the battery packs, allowing for longer run times without recharging and improving overall system efficiency. The problem of considering the length of the path for energy consumption per given gradient of the motion profile was solved. Full article

A hybrid mining dump truck contains multiple power sources and has a variety of operating modes. When the vehicle switches between different operating modes, inappropriate control strategies will result in insufficient power or excessive output torque ripple. The resulting vehicle shock and the dynamic characteristics of the dynamic clutch mechanism will affect ride comfort. In order to improve the performance of the hybrid mining dump truck in the mode switching process as much as possible, this paper takes power-split hybrid special transmission as the research object and proposes a hybrid model predictive control (HMPC) strategy. However, the simulation time of the HMPC algorithm is about 27% less than that of the MPC algorithm. HMPC can significantly shorten the control time while improving the vehicle ride comfort, reducing the sliding friction work during mode switching, and improving the real-time and robustness of mode switching. Full article

Unmanned underground articulated dump trucks (UADTs) are an important direction for the coal mining industry to vigorously promote automation and intelligence. Among these, tracking and controlling the motion trajectory is the key weak link. This paper presents a kinematic analysis of the stationary turning process of UADTs. Then, a posture state model for articulated trucks is established. The objective is to optimize the control method and further improve trajectory tracking accuracy. Based on the advantages and disadvantages of the feedback linearization control (FLC) method, a sliding mode control method based on the Ackermann formula (ASMC) and integral type switch gain (ISMC) are proposed. Finally, hardware-in-the-loop simulation verifies the superiority and tracking quality of the controller. The results show that the ASMC controller can control the lateral position deviation, course angle deviation, and curvature deviation around 10 cm, 0.04 rad, and 0.08 m⁻¹ in the hardware-in-the-loop simulation environment. The ISMC controller can control the lateral position deviation, course angle deviation, and curvature deviation near 8 cm, 0.01 rad, and 0.02 m⁻¹, and can also effectively control the jitter problem. Each deviation is stabilized within 10 s. This provides a reference for the development of trajectory tracking strategies for articulated vehicles. Full article

Mining dump trucks play an important role in engineering construction and resource extraction. Current research mainly focuses on the dynamic modeling and reliability analysis of the vehicle frame, suspension and overall model. However, with the development of electric drive, the wheel hub system has become an important component in mining truck equipment. This paper investigates the multi-body modeling of a mining truck’s hub drive reduction system in order to analyze its output characteristics including the stability of the angular velocity of its planetary carriers and the fluctuations in its meshing forces. A bench experiment was also conducted to verify the accuracy and stability of the proposed modeling. And the simulation results revealed that the fluctuations in the angular velocity of the planetary carriers were primarily influenced by the excitation from the hub motor’s input and the meshing forces between the gears of the reducers, which were mainly determined by the contact stiffness, damping, and clearance value during gear contact. Full article

To achieve a rational allocation of real-time operational equipment, such as excavators and dump trucks, in open-pit mines, and thereby enhance truck–shovel coordination, this paper addresses the challenges posed by unreasonable on-site scheduling, which includes excessive truck waiting times and prolonged excavator boom-and-dipper operations. Ultimately, the paper aims to attain optimal truck–shovel coordination efficiency. To this end, we construct a scheduling optimization model, with the production capacities of trucks and shovels serving as constraints. The objective functions of this model focus on minimizing transportation costs, reducing truck waiting times, and shortening excavator boom-and-dipper operation durations. To solve this model, we have developed an improved genetic algorithm that integrates roulette wheel selection and elite preservation strategies. The experimental results of our algorithm demonstrate that it can provide a more refined operational equipment scheduling scheme, effectively decreasing truck transportation costs and enhancing equipment utilization efficiency in open-pit mines. Full article

This paper takes the dual-motor pure electric mining dump truck as the research object and designs a dual-motor rear-drive torque optimization allocation strategy in view of the problems such as the large load variation of the dump truck and the facts that the motor output torque cannot accurately express the driver’s dynamic intention and that the overall output efficiency of the dual motor is low. The strategy first divides the demand torque of the whole vehicle into two parts, the base torque and the compensation torque, which are determined by the accelerator pedal opening and the motor speed, and the compensation torque is fuzzy-controlled by taking the vehicle speed, the rate of change of the accelerator pedal opening, and the state of the battery charge as inputs. Subsequently, the dual-motor drive torque allocation is optimized using a particle swarm algorithm, with the objective of minimizing power loss in the dual motors. Furthermore, the energy-saving effect of the torque optimization allocation strategy proposed in this paper is compared with that of the traditional torque average allocation strategy under three working conditions: the driving conditions of Chinese dump trucks, the unloaded uphill movement of mining dump trucks, and the fully loaded downhill movement of mining dump trucks. The results show that the average efficiency of the dual-motor drive using the torque optimization allocation strategy is improved by 2.32%, 4.23%, and 2.24%, respectively, and battery energy savings are improved by 0.5%, 0.47%, and 0.24%, respectively. Full article

The present work is devoted to the analysis of the most important reliability indicators of components of electrical devices of mining dump trucks, and analytical methods of their evaluation are proposed. A mathematical model for calculating the reliability of electrical devices integrated into the electrical systems of quarry dump trucks is presented. The model takes into account various loads arising in the process of operation and their influence on reliability reduction. Optimisation of maintenance and repair schedules of electrical equipment has revealed problems for research. One of them is the classification of electrical equipment by similar residual life, which allows the formation of effective repair and maintenance cycles. The analysis of statistical data on damages revealed the regularities of their occurrence, which is an important factor in assessing the reliability of electrical equipment in mining production. For quantitative assessment of reliability, it is proposed to use the parameter of the average expected operating time per failure. This parameter characterises the relative reliability of electrical equipment and is a determining factor of its reliability. The developed mathematical model of equipment failures with differentiation of maintained equipment by repeated service life allows flexible schedules of maintenance and repair to be created. The realisation of such cycles makes it possible to move from planned repairs to the system of repair according to the actual resource of the equipment. Full article

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes. A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for the safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31 m³/t. These results demonstrate that unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines. Full article

With the advancement of technology, mining trucks are gradually becoming larger, imposing higher performance requirements on the front suspension. There is a need to transform the original integral non-independent front axle of mining dump trucks with a payload exceeding 300 tons into an independent front suspension with a double-wishbone suspension. The changing of the front suspension is bound to have an impact on the overall vehicle’s handling stability and ride comfort. Therefore, the following research is conducted to investigate and analyze these effects. Firstly, the paper proposes a method for optimizing the parameters of the double-wishbone front suspension. The double-wishbone front suspension is modeled, and a comparison with a kinematic model is conducted to validate the accuracy of the model. Secondly, unreasonable hardpoint parameters are optimized. Thirdly, a dynamic model of the entire vehicle is established based on the optimized parameters, and an analysis of handling stability and ride comfort for the entire vehicle is performed. Finally, simulation results are compared and analyzed against experimental data. The results indicate that the optimized positioning parameters not only effectively enhance the suspension performance of the mining dump truck but also meet the requirements for handling stability and smoothness. The overall smoothness of the vehicle is significantly improved after the modification. This study not only holds significant engineering value in reducing vibrations in dump trucks and enhancing driver comfort, but also provides theoretical support for subsequent research and development in the industry. Full article

The sustainable development and operation of mining enterprises as major sources of economic growth in many countries is determined by a balance of economic, environmental, and social objectives and the resources required to achieve these objectives. Transportation is one of the main equipment at open pits. It accounts for up to 70% of open pit operating expenses. Various parameters of transportation means should be coordinated both with the parameters of the open pit and meet the conditions of its operation. The authors have developed a universal system of criteria for selecting a dump truck model for open pit conditions. The novelty of the proposed system lies in the complex consideration of all known factors to date and criteria of dump truck selection and the allocation of four groups of criteria: technical, technological, environmental, economic, and organizational. The groups of criteria and specific criteria in each group were ranked by the degree of importance using the multi-criteria decision-making method FUCOM (Full Consistency Method). The methodology of creating criteria systems in conditions of influence on the choice of alternatives of a set of interrelated factors is presented. The peculiarity of the methodology is the evaluation of different sets of criteria by several groups of experts with different competencies. The obtained criterion ranks are recommended to be used by managers of mining enterprises to choose a dump truck model. The presented methodology is suitable for the development of new systems of criteria, considering significant changes in operating conditions or the emergence of factors not considered in this study. Evaluation of all models of dump trucks on the market using the developed system of criteria is envisaged by the authors in a future study. Full article