Memory cutting of adjacent coal seams based on a hidden Markov model

Li, Wenyuan; Luo, Chengming; Yang, Hai; Fan, Qigao

doi:10.1007/s12517-013-1145-5

Cited by 30 publications

(14 citation statements)

References 18 publications

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“…Finally, the structural elements data and transition probabilities are used as a priori information to compute the posterior distribution information of the data through the GPR algorithm, predicting the coal seam thickness. The proposed method addresses the problem of the spatial distribution of coal seams, which is ignored in the existing coal seam prediction studies [15], improving the prediction accuracy.…”

Section: Ttp-gpr For Coal Seam Thickness Predictionmentioning

confidence: 99%

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Kang

Zhang

et al. 2019

Applied Sciences

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Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data that is obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m, which significantly improves the prediction accuracy in comparison to the existing back propagation (BP) neural networks, support vector machine, and Gaussian process regression methods.

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Section: Ttp-gpr For Coal Seam Thickness Predictionmentioning

confidence: 99%

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Kang

Zhang

et al. 2019

Applied Sciences

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“…At present, the researches on the cutting path planning of the shearer are mainly based on the accurate positioning of the shearer to obtain memory cutting [11,12], and automatic adjustment of drum based on intelligent decision-making or coal rock recognition [13]. Li Wei [14] proposed a hidden Markov model (HMM) memory cutting method for the shearer, which prevented large residual errors and frequent adjustments of the shearer cutting drum. Gospodarczyk Piotr [15] focused on the coal mining process for different variants of the shearer construction and kinematic parameters.…”

Section: Introductionmentioning

confidence: 99%

Cutting Path Planning Technology of Shearer Based on Virtual Reality

Liu

Xie

et al. 2020

Applied Sciences

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With regards to the low degree of digitization, lack of real geological terrain, and low degree of automation in the cutting process of the traditional virtual fully mechanized mining face, we studied the key technologies of virtual operation and cutting path planning of the shearer on the three-dimensional (3D) roof and floor based on the virtual reality engine (Unity3D). Firstly, the virtual 3D coal seam was constructed through the 3D geological coordinate data of the mine. On this basis, the shape function of the scraper conveyor with the adaptive configuration on the floor was constructed to obtain the combined operation of the virtual shearer and the scraper conveyor. The movement of the shearer’s walking and height-adjustment was then, analyzed. A strategy for automatic height-adjustment based on the adjustment of the direction of the drum movement is hence, proposed to control the cutting path of the shearer. Finally, different experimental schemes were simulated in the developed prototype system after which each of the schemes was evaluated using the fuzzy comprehensive evaluation method. The results show that the proposed strategy for trajectory control can improve the accuracy and stability of the shearer’s motion trajectory. In Unity3D, the pre-selected schemes and digital and visual planning of coal production are previewed ahead of time, the whole production process can be mapped synchronously in the production process. It is also obtained that the virtual preview and evaluation of the production process can provide some guidance for actual production.

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“…In the coal exploitation field, the notion that the mechanization and automatization of mining equipment are the basic factors for less manned or unmanned mining has gained widespread acceptance. Furthermore, the precise location awareness of the shearer is one of the key technologies for the automation of mining machines [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Error Compensation Property of Strapdown Inertial Navigation System Using Dynamic Model of Shearer

Yang

Luo

et al. 2016

IEEE Access

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The strapdown inertial navigation system (SINS) can be installed on a shearer and used for monitoring its position. However, under the complex environment of the mechanized mining face, the strong vibration of the shearer may cause large calculation error. First, the dynamic model of a doubledrum shearer is built with a force analysis, and the spectrum characteristics of linear vibration and angular vibration for the fuselage are then obtained. Second, the coning error and sculling error compensation models of SINS for the shearer are derived based on vibration characteristics. Meanwhile, according to the factor of the uncompensated model, multi-sample compensation model, and different coal and rock traits and different vibration frequencies of the fuselage, the shearer SINS error compensation property under multiple parameters is researched and analyzed in simulation. Finally, simulations indicate that the SINS error compensation model with the three-sample algorithm and four-sample algorithm can improve the calculating accuracy of the shearer SINS. The coning and sculling errors can be compensated effectively by the shearer error compensation model under many vibration conditions, such as different coal and rock traits and different frequencies of the fuselage.INDEX TERMS Strapdown inertial navigation system (SINS), error compensation, accuracy analysis, dynamic model of shearer, vibration characteristic.

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Memory cutting of adjacent coal seams based on a hidden Markov model

Cited by 30 publications

References 18 publications

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Cutting Path Planning Technology of Shearer Based on Virtual Reality

Research on Error Compensation Property of Strapdown Inertial Navigation System Using Dynamic Model of Shearer

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