Progress towards longwall automation

Kelly, Michael; Hainsworth, D.W.; Reid, David; Caris, Con; Gurgenci, Hal

doi:10.1201/9780203022528-147

Cited by 11 publications

(10 citation statements)

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“…X − X min X max − X min (11) where X, X max , X min are the index parameter, and the maximum and minimum value of the index, respectively. X' is the normalized value of the index parameter.…”

Section: =mentioning

confidence: 99%

“…Two key problems facing longwall mining automation are that of machine and face guidance to ensure the machines cut in the seam, and the face is kept straight [7,8]. Face alignment [9][10][11], produced from the shearer positioning with an inertial navigation system (INS), has been researched and applied widely in many coal mines [12,13]. It can keep the face straight for consistent production and minimal wear of the conveyor system.…”

Section: Introductionmentioning

confidence: 99%

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Longwall Face Automation: Coal Seam Floor Cutting Path Planning Based on Multiple Hierarchical Clustering

Guan,

Wang,

Wang

et al. 2023

Applied Sciences

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Space adaptability between mining equipment and coal-rock mass, to ensure the machines cut in a coal seam, is an importance technique in longwall mining automation. In order to guide the mining equipment cutting in the coal seam, a cutting path planning method based on multiple hierarchical clustering was proposed. Morphology similarity and the coplanarity measurement method were defined to evaluate the similarity of clusters. The coal seam floor series in the face-advancing direction were clustered according to the morphology similarity and coplanarity, respectively. Taking the morphology-based and coplanarity-based cluster centers as generating lines and stretching angle, respectively, the coal seam floor was reconstructed. The reconstructed floor can be regarded as the cutting path. The coal seam geological model of the 18,201 longwall face was analyzed with the proposed cutting path planning method. Comparing the reconstructed floor and original floor, the amounts of coal left and cut gangue were 1999 m3 and 1856 m3, respectively, for the segmental floor. For the case of whole floor, the amounts of coal left and cut gangue were 5642 m3 and 5463 m3, respectively. The coal loss rates only were 0.57% and 0.87% for the segmental and whole coal seam, respectively.

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Section: =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Longwall Face Automation: Coal Seam Floor Cutting Path Planning Based on Multiple Hierarchical Clustering

Guan,

Wang,

Wang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The key of the correct registration of virtual-real image is to precisely determine the projection coordinates in the scene image of each point on the virtual object. Under the uneven inferior coordinates expression, in the three dimensions space, the transformation relation between the point [x 0 , y 0 , z 0 , w 0 ] T on virtual object and the projection point [u, v, h] T in the enhanced scene plane may be expressed as in (1).…”

Section: Combining Vr and Ar Objects Registrationmentioning

confidence: 99%

Augmented Reality on Long-wall Face for Unmanned Mining

Zhang¹

2011

JCP

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<p>An unmanned mining technology for the fully mechanized longwall face automation production is proposed and studied. The essential technology will bring the longwall face production into visualization through the VR (Virtual Reality) and AR (Augmented Reality) combination. Based on the visual theoretical model of the longwall face, the combination of virtual and reality, the real-time interactive and the 3D registration function were realized. The 3D image of the longwall face may be scaled and viewed from free angles. Using the overall affine coordinate system, the stereoscopic impression for the longwall face was enhanced; the video image is matched to 3D characteristics; the occlusion issue is resolved with the depth information solution; and the simplification visualization interactive method is proposed. The Key technology and Alpha channel are used to the combination of the real longwall face and the virtual user.</p>

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“…The intelligent technology of straightness perception and straightening of the scraper conveyor mainly included the shearer inertial navigation system technology [3,4], the machine vision measurement technology [5], the digital twin technology [6,7], and the optical fiber shape sensing technology [8]. In 2003, the Commonwealth Scientific and Industrial Research Organization proposed an Longwall Automation Steering Committee (LASC) system based on strap-down inertial navigation technology [9]. In 2016, the LASC system began an industrial test in the Zhuanlongwan coal mine of Shandong Energy Group Co., Ltd, realizing the first three-dimensional attitude monitoring and spatial positioning of domestic shearers [10].…”

Section: Introductionmentioning

confidence: 99%

Scraper conveyor shape sensing technology based on orthogonal optical fiber strain

Chen

Fang

Feng

et al. 2023

Meas. Sci. Technol.

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Intelligent perception of a scraper conveyor straightness and attitude monitoring of mechanical supporting equipment in the stope have practical and theoretical values for mining. This study proposed an optical fiber curvature sensor and a scraper conveyor’s curve reconstruction method. The optical fiber curvature sensor comprises the fiber grating strain sensing optical cables, the flexible substrate, and the packaging material. The coordinate positions of each monitoring point are obtained through the strain-curvature conversion relationship and the slope recurrence algorithm, and then the reconstruction curve is obtained by fitting. The finite element simulation verifies the feasibility of the curve reconstruction method used for the deformation monitoring via optical fiber curvature sensors. The reconstruction error analysis results show that the root mean square error of reconstructions for two kinds of 2D plane bending and 3D space bending are 2.98, 1.89, and 3.13%, respectively. Their mean absolute errors are 8.9, 3.56, and 9.82 mm, respectively, verifying the feasibility and high accuracy of the proposed curve reconstruction equation. The research results provide a theoretical basis for the shape perception and straightening control of scraper conveyors in the intelligent working surface.

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Progress towards longwall automation

Cited by 11 publications

References 0 publications

Longwall Face Automation: Coal Seam Floor Cutting Path Planning Based on Multiple Hierarchical Clustering

Longwall Face Automation: Coal Seam Floor Cutting Path Planning Based on Multiple Hierarchical Clustering

Augmented Reality on Long-wall Face for Unmanned Mining

Scraper conveyor shape sensing technology based on orthogonal optical fiber strain

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