Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.0
2.1
Journals
Machines
Special Issues
Key Technologies in Intelligent Mining Equipment
share announcement

Key Technologies in Intelligent Mining Equipment

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 13606

Special Issue Editors

Dr. Lei Si

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: pose accurate perception; autonomous navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Jianbo Dai

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
Interests: coal mine efficient intelligent mining technology and equipment Intelligent mining robot

Special Issue Information

Dear Colleagues,

In recent years, there has been a significant surge in the development and adoption of intelligent mechanical equipment across various industries. Intelligent mechanical equipment, empowered by cutting-edge technologies such as artificial intelligence, machine learning, the Internet of Things, and robotics, has revolutionized traditional manufacturing, construction, transportation, and other sectors. This special issue aims to explore the latest advances, challenges, and applications of intelligent mining equipment, providing a platform for researchers, engineers, and practitioners to share their insights and experiences.

Intelligent Control Systems: Novel control algorithms and strategies for intelligent mining equipment, including adaptive control, predictive control, and reinforcement learning-based control.

Artificial Intelligence and Machine Learning: Applications of AI and machine learning techniques in intelligent mining equipment, such as pattern recognition, fault diagnosis, and predictive maintenance.

Sensing and Perception: Advanced sensors and perception technologies for intelligent mining equipment, including vision-based systems, LiDAR, and sensor fusion techniques.

Human-Machine Interaction: Design principles and technologies for enhancing human-machine interaction in intelligent mining equipment, including augmented reality interfaces and collaborative robotics.

Autonomous and Semi-autonomous Systems: Development and deployment of autonomous and semi-autonomous systems in manufacturing, agriculture, logistics, mining and other domains.

Safety and Reliability: Methods and technologies for ensuring the safety and reliability of intelligent mining equipment, including risk assessment, fault tolerance, and safety standards compliance.

Case Studies and Applications: Real-world case studies, applications, and best practices of intelligent mining equipment in various industries, highlighting their impact on efficiency, productivity, and sustainability.

Dr. Lei Si
Dr. Jianbo Dai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent control systems of mining equipment
  • artificial intelligence and machine learning in mining sensing and perception of mining sensors
  • human-machine interaction of mining robot
  • safety and reliability of mining technologies
  • case studies and applications of intelligent mining

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (8 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 4864 KiB  
Article
Intelligent Decision Framework for Booster Fan Optimization in Underground Coal Mines: Hybrid Spherical Fuzzy-Cloud Model Approach Enhancing Ventilation Safety and Operational Efficiency
by Shibin Yao, Jian Zhou, Manoj Khandelwal, Abiodun Ismail Lawal, Chuanqi Li, Moshood Onifade and Sangki Kwon
Machines 2025, 13(5), 367; https://doi.org/10.3390/machines13050367 - 29 Apr 2025
Abstract
Optimizing mine fan operations in underground coal mines is important for ensuring proper ventilation, enhancing safety, and improving operational efficiency. A single main ventilation fan is insufficient to meet the ventilation demands of the entire mine. Therefore, it is necessary to consider the [...] Read more.
Optimizing mine fan operations in underground coal mines is important for ensuring proper ventilation, enhancing safety, and improving operational efficiency. A single main ventilation fan is insufficient to meet the ventilation demands of the entire mine. Therefore, it is necessary to consider the addition of booster fans to ensure effective ventilation. However, the selection of booster fans involves multiple influencing factors, and the complex interrelationships among fans remain unclear, making solution selection and risk assessment more challenging. To address this issue, this study proposes an optimization and risk analysis method for booster fan selection based on an improved analytic hierarchy process. This method leverages spherical fuzzy sets to handle uncertainty in the ventilation parameters and cloud models to facilitate probabilistic decision making. Through this model, the important relationships of the influencing factors for fan selection can be systematically determined, allowing for a rational assessment of the performance scores of candidate solutions. It provides a ranking of the alternatives based on their superiority, along with the risk indicators and optimization potentials of the selected solution. Ultimately, the reliability of the chosen model was verified through comparison and validation. This method not only enhances the scientific and rational basis for booster fan selection, reducing the complexity of the selection process, but also provides theoretical support for the optimization of coal mine ventilation systems. This study demonstrates the model’s effectiveness at improving ventilation safety and cost efficiency, making it a valuable tool for modern underground mining operations. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

15 pages, 1472 KiB  
Article
Intelligent Scheduling in Open-Pit Mining: A Multi-Agent System with Reinforcement Learning
by Gabriel Icarte-Ahumada and Otthein Herzog
Machines 2025, 13(5), 350; https://doi.org/10.3390/machines13050350 - 23 Apr 2025
Viewed by 118
Abstract
An important process in the mining industry is material handling, where trucks are responsible for transporting materials extracted by shovels to different locations within the mine. The decision about the destination of a truck is very important to ensure an efficient material handling [...] Read more.
An important process in the mining industry is material handling, where trucks are responsible for transporting materials extracted by shovels to different locations within the mine. The decision about the destination of a truck is very important to ensure an efficient material handling operation. Currently, this decision-making process is managed by centralized systems that apply dispatching criteria. However, this approach has the disadvantage of not providing accurate dispatching solutions due to the lack of awareness of potentially changing external conditions and the reliance on a central node. To address this issue, we previously developed a multi-agent system for truck dispatching (MAS-TD), where intelligent agents representing real-world equipment collaborate to generate schedules. Recently, we extended the MAS-TD (now MAS-TDRL) by incorporating learning capabilities and compared its performance with the original MAS-TD, which lacks learning capabilities. This comparison was made using simulated scenarios based on actual data from a Chilean open-pit mine. The results show that the MAS-TDRL generates more efficient schedules. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

17 pages, 12748 KiB  
Article
Fault Feature Extraction Based on Variational Modal Decomposition and Lifting Wavelet Transform: Application in Gear of Mine Scraper Conveyor Gearbox
by Zhengxiong Lu, Linyue Li, Chuanwei Zhang, Shuanfeng Zhao and Lingxiao Gong
Machines 2024, 12(12), 871; https://doi.org/10.3390/machines12120871 - 30 Nov 2024
Cited by 2 | Viewed by 764
Abstract
Vibration-based fault diagnosis of chain conveyor gearboxes is challenging under high load and strong shock conditions. This paper applies motor current characteristic analysis technology to scraper conveyor gearbox fault diagnosis and proposes a fault feature extraction method. Firstly, a variational mode decomposition algorithm [...] Read more.
Vibration-based fault diagnosis of chain conveyor gearboxes is challenging under high load and strong shock conditions. This paper applies motor current characteristic analysis technology to scraper conveyor gearbox fault diagnosis and proposes a fault feature extraction method. Firstly, a variational mode decomposition algorithm combined with a genetic algorithm is used to divide the original current signal into several sub-bands with different frequency modulation information, and irrelevant information is preliminarily eliminated. Secondly, an intrinsic mode function (IMF) sub-band fault information extraction method based on lifting wavelet transform is proposed. The minimum entropy value is used to set the sensitive parameters involved in lifting wavelet transform, and the power supply current frequency and noise interference information of a scraper conveyor are removed from the current signal. Finally, it is proved that variational mode decomposition combined with lifting wavelet transform can effectively diagnose the fault of a scraper conveyor by comparative experiments. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

15 pages, 4366 KiB  
Article
Research on Feedforward-Feedback Composite Anti-Disturbance Control of Electro-Hydraulic Proportional System Based on Dead Zone Compensation
by Jianbo Dai, Haozhi Xu, Lei Si, Dong Wei, Jinheng Gu and Hang Chen
Machines 2024, 12(12), 855; https://doi.org/10.3390/machines12120855 - 27 Nov 2024
Viewed by 886
Abstract
Considering the complexity and difficulty of obtaining certain parameters in the electro-hydraulic proportional control system, a precise transfer function of the system was derived through parameter identification using experimental data obtained from an Amesim simulation model after establishing a basic mathematical model. This [...] Read more.
Considering the complexity and difficulty of obtaining certain parameters in the electro-hydraulic proportional control system, a precise transfer function of the system was derived through parameter identification using experimental data obtained from an Amesim simulation model after establishing a basic mathematical model. This approach reduces the reliance on accurate parameters of individual components. A feedforward-feedback composite controller was designed, and its effectiveness was validated in Simulink using the system’s transfer function. Subsequently, the dead zone range of the proportional valve was determined through experiments, and a dead zone compensation strategy was designed, which reduced the time required for the proportional valve to traverse the dead zone by 89.4%. Based on the dead zone compensation, trajectory tracking experiments were conducted to validate the effectiveness of the feedforward-feedback composite controller. Under fixed disturbances, the trajectory tracking error was reduced by 53.8% compared to feedback control. Under time-varying load disturbances, the trajectory tracking error was reduced by 51.2% compared to feedback control. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

17 pages, 6160 KiB  
Article
Research on Velocity Feedforward Control and Precise Damping Technology of a Hydraulic Support Face Guard System Based on Displacement Feedback
by Qingliang Zeng, Yulong Hu, Zhaosheng Meng and Lirong Wan
Machines 2024, 12(10), 676; https://doi.org/10.3390/machines12100676 - 27 Sep 2024
Cited by 1 | Viewed by 1050
Abstract
The hydraulic support face guard system is essential for supporting the exposed coal wall at the working face. However, the hydraulic support face guard system approaching the coal wall may cause impact disturbances, reducing the load-bearing capacity of coal walls. Particularly, the hydraulic [...] Read more.
The hydraulic support face guard system is essential for supporting the exposed coal wall at the working face. However, the hydraulic support face guard system approaching the coal wall may cause impact disturbances, reducing the load-bearing capacity of coal walls. Particularly, the hydraulic support face guard system is characterized by a large turning radius when mining thick coal seams. A strong disturbance and impact on the coal wall may occur if the approaching speed is too fast, leading to issues such as rib spalling. In this paper, a feedforward fuzzy PID displacement velocity compound controller (FFD displacement speed compound controller) is designed. The PID controller, fuzzy PID controller, feedforward PID controller, and FFD displacement speed compound controller are compared in terms of the tracking characteristics of the support system and the impact response of the coal wall, validating the controller’s rationality. The results indicate that the designed FFD displacement speed compound controller has significant advantages. This controller maintains a tracking error range of less than 1% for target displacement with random disturbances in the system, with a response adjustment time that is 34% faster than the PID controller. Furthermore, the tracking error range for target velocity is reduced by 8.4% compared to the feedforward PID controller, reaching 13.8%. Additionally, the impact disturbance of the support system on the coal wall is suppressed by the FFD displacement speed compound controller, reducing the instantaneous contact impact between the support plate and the coal wall by 350 kN. In summary, the FFD compound controller demonstrates excellence in tracking responsiveness and disturbance rejection, enhancing the efficacy of hydraulic supports, and achieving precise control over the impact on the coal wall. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

19 pages, 5885 KiB  
Article
Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era
by Kui Liu, Bin Mei, Qing Li, Shuai Sun and Qingping Zhang
Machines 2024, 12(6), 419; https://doi.org/10.3390/machines12060419 - 18 Jun 2024
Cited by 1 | Viewed by 1347
Abstract
Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for [...] Read more.
Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for a paradigm shift towards unmanned mining operations. With this study, we addressed the urgent need for safe and efficient production based on intelligent unmanned mining systems in open-pit mines. A collaborative production planning model was developed for an intelligent unmanned system comprising multiple excavators and mining trucks. The model is formulated to optimize multiple objectives, such as total output, equipment idle time, and transportation cost. A multi-objective optimization approach based on the genetic algorithm was employed to solve the model, ensuring a balance among conflicting objectives and identifying the best possible solutions. The computational experiments revealed that the collaborative production planning method significantly reduces equipment idle time and enhances output. Moreover, with the proposed method, by optimizing the configuration to include 6 unmanned excavators, 50 unmanned mining trucks, and 4 unloading points, a 92% reduction in excavator idle time and a 44% increase in total output were achieved. These results show the model’s potential to transform open-pit mining operations by using intelligent planning. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

17 pages, 4689 KiB  
Article
A Walking Trajectory Tracking Control Based on Uncertainties Estimation for a Drilling Robot for Rockburst Prevention
by Jinheng Gu, Shicheng He, Jianbo Dai, Dong Wei, Haifeng Yan, Chao Tan, Zhongbin Wang and Lei Si
Machines 2024, 12(5), 298; https://doi.org/10.3390/machines12050298 - 28 Apr 2024
Cited by 1 | Viewed by 1047
Abstract
A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode [...] Read more.
A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode chattering in the designed sliding mode controller. Additionally, a walking trajectory tracking control strategy is proposed to improve the walking trajectory tracking performance in terms of response time, tracking precision, and robustness, including walking longitudinal and lateral trajectory tracking controllers. Finally, simulation and experimental results are employed to verify the trajectory tracking performance and observability of the model uncertainties. The results testify that the proposed approach is better than other comparative methods, and the longitudinal and lateral trajectory tracking average absolute errors are controlled in 10.23 mm and 22.34 mm, respectively, thereby improving the walking trajectory tracking performance of the walking electrohydraulic control system for the coal mine drilling robot for rockburst prevention. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

Review

Jump to: Research

17 pages, 285 KiB  
Review
Equipment and Operations Automation in Mining: A Review
by Michael Long, Steven Schafrik, Peter Kolapo, Zach Agioutantis and Joseph Sottile
Machines 2024, 12(10), 713; https://doi.org/10.3390/machines12100713 - 9 Oct 2024
Cited by 4 | Viewed by 7481
Abstract
The mining industry is undergoing a transformative shift driven by the rapid advancement and adoption of automation technologies. This paper provides a comprehensive overview of the current state of automation in mining, examining the technological advancements, their applications, and the prospects of automation [...] Read more.
The mining industry is undergoing a transformative shift driven by the rapid advancement and adoption of automation technologies. This paper provides a comprehensive overview of the current state of automation in mining, examining the technological advancements, their applications, and the prospects of automation in this critical industry. A key focus of this paper is the impact of automation on the safety and efficiency of mining operations. Highlighting the successful implementation of Automated Haul Truck Systems (AHSs) in surface mining. Additionally, this paper explores the development of automation in underground mining and its challenges, particularly limitations in communication and localization, which hinder the development and deployment of fully autonomous systems. It also provides an exploration of the challenges associated with widespread automation adoption in mining, including high initial investment costs, concerns about job displacement, and the need for specialized skills and training. Looking toward future advancements in enabling technologies will be critical for furthering automation in mining. Machine learning and AI will play an increasingly critical role in intelligent automation, enabling autonomous systems to adapt to dynamic environments, optimize processes, and make informed decisions. This paper provides a look into human–robot collaboration in the future of mining. As the industry transitions toward greater automation, it is essential to consider the evolving roles of human workers to foster a collaborative work environment. This involves prioritizing human safety, providing adequate training, and addressing concerns about job displacement to ensure a smooth transition toward a more automated future. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-8
Back to TopTop