Coal–rock interface detection on the basis of image texture features

Sun, Jun; Su, Bo

doi:10.1016/j.ijmst.2013.08.011

Cited by 40 publications

(26 citation statements)

References 8 publications

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“…A geometric track cuttingmemory method has been applied in several mining shearers, but this approach only achieves the desired in-seam control over a small number of shearer cycles as it cannot take into account the higher than first-order trends and shortterm variations in the seam horizon [7]. Some researchers have proposed drum height adjustment based on a coal interface detection method using natural gamma radiation, vibration signal, thermal infrared rays, and other pieces of sensor information to identify coal and rock as mentioned in [6][7][8][9][10]. However, the coal interface detection methods have never been successfully applied because of the complicated and unpredictable geological conditions in a coal-mining face.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model and Error Analysis of Shearer Cutting Path Based on Its Attitude

Wang

2018

Mathematical Problems in Engineering

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The horizon control system is the key technology in the automation of a shearer. The achievement of accurate shearer cutting path plays an important role for horizon control. A mathematical model of cutting path in the local geographic coordinate frame was built. Error analysis based on genetic algorithm (GA) was studied to guarantee the accuracy of the shearer cutting path. Parameters from a MG1000/2660-WD shearer and data from a working face were used to obtain the shearer cutting path with reference to the local geographic coordinate frame. Also, with error analysis based on GA, the desired sensors were chosen, which allowed coordinate position errors of a shearer’s cutting path to be less than 0.01 m. The desired accuracies of the inertial navigation system and encoders mounted on the different shearers used in thin seam, medium-thickness seam, and thick seam were calculated.

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Section: Introductionmentioning

confidence: 99%

A Mathematical Model and Error Analysis of Shearer Cutting Path Based on Its Attitude

Wang

2018

Mathematical Problems in Engineering

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“…The representative methods include detection through cutting force response [5], image [6,7], vibration acoustic [8][9][10][11][12][13][14][15], and infrared [16]. Cutting force response detection using drum picks will have different performance cutting coal or rock to identify coal and rock.…”

Section: Introductionmentioning

confidence: 99%

Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform

Zhang¹,

Zeng-cai²,

Zhao³

et al. 2017

Shock and Vibration

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This study employs the mechanical vibration and acoustic waves of a hydraulic support tail beam for an accurate and fast coal-rock recognition. The study proposes a diagnosis method based on bimodal deep learning and Hilbert-Huang transform. The bimodal deep neural networks (DNN) adopt bimodal learning and transfer learning. The bimodal learning method attempts to learn joint representation by considering acceleration and sound pressure modalities, which both contribute to coal-rock recognition. The transfer learning method solves the problem regarding DNN, in which a large number of labeled training samples are necessary to optimize the parameters while the labeled training sample is limited. A suitable installation location for sensors is determined in recognizing coal-rock. The extraction features of acceleration and sound pressure signals are combined and effective combination features are selected. Bimodal DNN consists of two deep belief networks (DBN), each DBN model is trained with related samples, and the parameters of the pretrained DBNs are transferred to the final recognition model. Then the parameters of the proposed model are continuously optimized by pretraining and fine-tuning. Finally, the comparison of experimental results demonstrates the superiority of the proposed method in terms of recognition accuracy.

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“…It was first applied in the 1940s in Russia and then subsequently used in France, Turkey, former Yugoslavia, Romania, Hungary, and former Czechoslovakia [ 2 , 3 ]. As the development of modern mining equipments, hydraulic support, conveyor, shearer and so on are widely used in coal working face [ 4 ], Coal-rock recognition(CRR) is one of the critical technique on TCC automation in fully mechanized top coal caving face [ 5 ]. Since the 1960s, more than 30 coal-rock recognition methods have been put forward, these methods covered gamma radiation, radar, vibration, infrared radiation, stress, acoustic, and so on[ 5 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…As the development of modern mining equipments, hydraulic support, conveyor, shearer and so on are widely used in coal working face [ 4 ], Coal-rock recognition(CRR) is one of the critical technique on TCC automation in fully mechanized top coal caving face [ 5 ]. Since the 1960s, more than 30 coal-rock recognition methods have been put forward, these methods covered gamma radiation, radar, vibration, infrared radiation, stress, acoustic, and so on[ 5 – 8 ]. MOWREY [ 6 ] developed a detecting coal interface method during the mining operation based on the continually monitor of mining machine.…”

Section: Introductionmentioning

confidence: 99%

“…Based on Mel-frequency cepstrum coefficient (MFCC) and neural network, Xu et al [ 8 ] proposed a coal-rock interface recognition method during top -coal caving by acoustic sensors which were fixed on the tail beam of hydraulic support. Sun and Su [ 5 ] proposed a coal-rock interface detection method for the top-coal caving face on the digital image gray level co-occurrence matrix and fisher discriminant technique. Combining image feature extraction, Hou W. [ 9 ], Reddy & Tripathy [ 10 ] gave their coal-gangue automated separation systems for the row coal in the conveyor belt transporting.…”

Section: Introductionmentioning

confidence: 99%

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Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

et al. 2017

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Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

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Coal–rock interface detection on the basis of image texture features

Cited by 40 publications

References 8 publications

A Mathematical Model and Error Analysis of Shearer Cutting Path Based on Its Attitude

A Mathematical Model and Error Analysis of Shearer Cutting Path Based on Its Attitude

Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

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