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Assessment of Rib Spalling Hazard Degree in Mining Face Based on Background Subtraction Algorithm and Support Vector Machine


Affiliations
1 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
 

Rib spalling is one of the common hazards in a fully mechanized mining face. In order to accurately assess the hazard degree, this study proposes a new method based on background subtraction algorithm and support vector machine (SVM). First, the architecture diagram of rib spalling feature analysis is constructed, and the rib spalling feature indices are determined, including the duration area, height and the centre of ribs spalling height. Then, the specific feature analysis process of rib spalling is performed using the background subtraction algorithm. Furthermore, some virtual 3D rib spalling animations are generated using 3D Studio Max (3Ds Max) software to verify the reasonability of extracted features. Thereafter, the assessment model of rib spalling hazard degree is established based on SVM. Three assessment models based on SVM, back propagation neural network (BPNN) and artificial immune (AI) algorithm have been developed. The assessment accuracy of SVM (reaching 85%) is obviously higher than that of BP-NN (75%) and AI (70%) algorithm. The results indicate the feasibility and superiority of the proposed method in the assessment of rib spalling hazard degree.

Keywords

Background Subtraction Algorithm, Hazard Degree Assessment, Mining Face, Rib Spalling, Support Vector Machine.
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  • Yao, Q. L. et al., Numerical investigation of the effects of coal seam dip angle on coal wall stability. Int. J. Rock Mech. Min., 2017, 100, 298-309.

  • Wang, Z. H., Yang, J. H. and Meng, H., Mechanism and controlling technology of rib spalling in mining face with large cutting height passing through fault. J. China Coal Soc., 2015, 40, 42-49.

  • Peng, R. et al., Experimental research on the structural instability mechanism and the effect of multi-echelon support of deep roadways in a kilometre-deep well. PLoS ONE, 2018, 13, e0192470.

  • Zhang, G. C. et al., Analysis of gateroad stability in relation to yield pillar size: a case study. Rock Mech. Rock Eng., 2017, 50, 1263-1278.

  • Likar, J. et al., Analysis of geomechanical changes in hanging wall caused by longwall multi top caving in coal mining. J. Min. Sci., 2012, 48, 135-145.

  • Bhaskaran, S. et al., Experimental studies on spalling characteristics of Indian lignite coal in context of underground coal gasification. Fuel, 2015, 154, 326-337.

  • Bai, Q. S. et al., Numerical modeling on brittle failure of coal wall in longwall face -a case study. Arab. J. Geosci., 2014, 7, 5067-5080.

  • Wang, J. C., Mechanism of the rib spalling and the controlling in the very soft coal seam. J. China Coal Soc., 2007, 32, 785-788.

  • Fang, X. Q., He, J. and Li, H. C., A study of the rib fall mechanism in soft coal and its control at a full-mechanized topcoal caving face. J. China Univ. Min. Technol., 2009, 38, 641-645.

  • Zhang, Y. L. et al., Effect analysis of rib spalling prevention system in hydraulic support. J. China Coal Soc., 2011, 36, 691-695.

  • Zhang, H. L., Wang, L. G. and Qin, H., Study of spalling mechanism and control techniques of mining roadway. Rock Soil Mech., 2012, 33, 1462-1466.

  • Zhang, S. Q., Technology research of excavating tunnel temporary support and prevent sloughing. Saf. Coal Mines, 2013, 22, 19-20.

  • Liu, G. F. et al., Failure characteristics, laws and mechanisms of rock spalling in excavation of large-scale underground powerhouse caverns in Baihetan. Chin. J. Rock Mech. Eng., 2016, 35, 865-878.

  • Jiang, Q. et al., Evaluation method of general geostress based on spalling features of wall rock. Rock Soil Mech., 2011, 32, 1452-1459.

  • Jeyabharathi, D. and Dejey, D., Efficient background subtraction for thermal images using reflectional symmetry pattern (RSP). Multimedia Tools Appl., 2018, 77, 22567-22586.

  • Sugimura, D., Teshima, F. and Hamamoto, T., Online background subtraction with freely moving cameras using different motion boundaries. Image Vision Comput., 2018, 76, 76-92.

  • Liu, X. et al., Background subtraction using spatio-temporal group sparsity recovery. IEEE T. Circuits Syst. Video Technol., 2018, 28, 1737-1751.

  • Goyal, K. and Singhai, J., Review of background subtraction methods using Gaussian mixture model for video surveillance systems. Artif. Intell. Rev., 2018, 50, 241-259.

  • Wu, J. D. and Tsai, Y. J., Speaker identification system using empirical mode decomposition and an artificial neural network. Exp. Syst. Appl., 2011, 38, 6112-6117.

  • Polat, K. and Güneş, S., An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit. Signal Process., 2007, 17, 702-710.

  • Si, L. et al., Identification of shearer cutting patterns using vibration signals based on a least squares support vector machine with an improved fruit fly optimization algorithm. Sensors, 2016, 16, 90.

  • Ukil, A., Support vector machine. Comput. Sci., 2002, 1, 1-28.

  • Shahrizat, S. M. and Nooritawati, M. T., Background modelling and background subtraction performance for object detection. Proceedings of the 6th International Colloquium on Signal Processing and its Applications, Melaka, Malaysia, 2010, pp. 236-241.

  • Sadykhov, R. K. and Kuchuk, S., Background substraction in grayscale images algorithm. In Proceedings of the 7th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Berlin, Germany, 2013, vol. 12, pp. 425-428.

  • Lin, H. H., Chuang, J. H. and Liu, T. L., Regularized background adaptation: a novel learning rate control scheme for Gaussian mixture modeling. IEEE Trans. Image Process., 2011, 20, 822-836.

  • Horprasert, T., Harwood, D. and Davis, L. S., A statistical approach for real-time robust background subtraction and shadow detection. In Proceedings of IEEE ICCV'99 Frame-Rate Workshop, 1999, pp. 1-19.

  • KaewTraKulPong, P. and Bowden, R., An improved adaptive background mixture model for real-time tracking with shadow detection. In Proceedings of the 2nd European Workshop on Advanced Video Based Surveillance Systems, Kingston upon Thames, UK, AVBS01, September 2001, pp. 1-5.

  • An, J., Zhang, G. C. and Liu, Y. N., Application of mathematical morphology in processing of medical eye images. Mech. Eng. Autom., 2016, 1, 15-17.

  • Chen, H. T., Lin, H. H. and Liu, T. L., Multi-object tracking using dynamical graph matching. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, 2003, p. 210.

  • Vapnik, V. N., The nature of statistical learning theory. IEEE Trans. Neural Network, 1995, 8, 988-999.

  • Karimi, M., A new approach to history matching based on feature selection and optimized least square support vector machine. J. Geophys. Eng., 2018, 15, 2378-2387.

  • Arenas, M. P. et al., Novel austenitic steel ageing classification method using eddy current testing and a support vector machine. Measurement, 2018, 127, 98-103.

  • Chen, Y. G., Prediction algorithm of PM2.5 mass concentration based on adaptive BP neural network. Computing, 2018, 100, 825-838.

  • Sadeghi, M., Maghooli, K. and Moin, M. S., Using artificial immunity network for face verification. Int. Arab. J. Inf. Technol., 2014, 11, 354-361.


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  • Assessment of Rib Spalling Hazard Degree in Mining Face Based on Background Subtraction Algorithm and Support Vector Machine

Abstract Views: 246  |  PDF Views: 83

Authors

Lei Si
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Zhongbin Wang
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Xinhua Liu
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Chao Tan
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Rongxin
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China

Abstract


Rib spalling is one of the common hazards in a fully mechanized mining face. In order to accurately assess the hazard degree, this study proposes a new method based on background subtraction algorithm and support vector machine (SVM). First, the architecture diagram of rib spalling feature analysis is constructed, and the rib spalling feature indices are determined, including the duration area, height and the centre of ribs spalling height. Then, the specific feature analysis process of rib spalling is performed using the background subtraction algorithm. Furthermore, some virtual 3D rib spalling animations are generated using 3D Studio Max (3Ds Max) software to verify the reasonability of extracted features. Thereafter, the assessment model of rib spalling hazard degree is established based on SVM. Three assessment models based on SVM, back propagation neural network (BPNN) and artificial immune (AI) algorithm have been developed. The assessment accuracy of SVM (reaching 85%) is obviously higher than that of BP-NN (75%) and AI (70%) algorithm. The results indicate the feasibility and superiority of the proposed method in the assessment of rib spalling hazard degree.

Keywords


Background Subtraction Algorithm, Hazard Degree Assessment, Mining Face, Rib Spalling, Support Vector Machine.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi12%2F2001-2012