Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review
Yuxin Du,
He Zhang,
Liang Liang
et al.
Abstract:The realization of intelligent mining is the only method for realizing high-quality development in the coal industry. As the forefront working link in mine production, achieving automatic roadway tunneling control is key to improving production efficiency, enhancing intelligence, and reducing accident rates at fully mechanized tunneling working faces. Among various detection techniques, machine vision technology stands out with advantages of non-contact measurement, rich information acquisition, and high detec… Show more
“…3D reconstruction of mine airways is another important direction in the development of AI technologies in both mine ventilation and geotechnology (Ren et al, 2019 ; Du et al, 2023 ). Machine vision was utilized to process measurement data of the geometry of mine airways and mining equipment using various sensors: visual, inertial, LiDAR, and their combinations (Artan et al, 2011 ; Jiang et al, 2019 ; Zhai et al, 2020 ; Singh et al, 2023 ).…”
Section: Application Areas Of Ai Technologiesmentioning
In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.
“…3D reconstruction of mine airways is another important direction in the development of AI technologies in both mine ventilation and geotechnology (Ren et al, 2019 ; Du et al, 2023 ). Machine vision was utilized to process measurement data of the geometry of mine airways and mining equipment using various sensors: visual, inertial, LiDAR, and their combinations (Artan et al, 2011 ; Jiang et al, 2019 ; Zhai et al, 2020 ; Singh et al, 2023 ).…”
Section: Application Areas Of Ai Technologiesmentioning
In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.
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