Abstract
Impact contact behaviors with different velocities, direction and contact states occurred in the process of coal drawing between coal gangue particles and the hydraulic support greatly increase the difficulty of coal gangue recognition in top coal caving. In order to finally realize coal gangue recognition in top coal caving, this paper studied the impact behavior of a single particle coal gangue and the metal plate at any position and the problem of the identification of coal gangue particles. First, the any position impacting test bench and test system is constructed, in which multipoint acceleration information acquisition space is built. Second, 2400 groups of random impact tests between coal gangue particle and the metal plate are conducted. Third, while each impact test was going on, acceleration signals in the seven different positions of the metal plate were collected. Forth, after all the impact tests are completed, signals are processed by segmentation and re-sorted by randomization. Then, the method of signals serial splicing is proposed to process the different position standardized signals obtained in each group of impact test, and coal gangue recognition is proposed based on processed signal data and SVM algorithm. After that, the effect of parameters on coal gangue recognition accuracy of SVM algorithm is studied, and the corresponding parameter settings of SVM are accordingly determined. Finally, coal gangue recognition was conducted by using SVM based on different data samples, the influence law of the training set data set expansion method and the test set data matching relationship, the number of tandem sensors and matching relationship, and the number of training set samples on coal gangue recognition accuracy were studied. The study will serve as the research basis for the application of SVM and signals serial splicing method in coal gangue recognition.
Similar content being viewed by others
References
Yu, B.; Zhao, J.; Xiao, H.T.: Case study on overburden fracturing during longwall top coal caving using microseismic monitoring. Rock Mech. Rock Eng. 50, 507–511 (2017). https://doi.org/10.1007/s00603-016-1096-8
Zhang, J.W.; Wang, J.C.; Wei, W.J.; Chen, Y.; Song, Z.Y.: Experimental and numerical investigation on coal drawing from thick steep seam with longwall top coal caving mining. Arab. J. Geosci. 11, 96 (2018). https://doi.org/10.1007/s12517-018-3421-x
Vakilin, A.; Hebblewhite, B.K.: A new cavability assessment criterion for Longwall Top Coal Caving. Int. J. Rock Mech. Min. Sci. 47, 1317–1329 (2010). https://doi.org/10.1016/j.ijrmms.2010.08.010
Lin, C.-C.; Deng, D.-J.; Chen, Zheng-Yu; Chen, K.-C.: Key design of driving industry 4.0: joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks. IEEE Commun. Magazine. 54(10), 46–52 (2016). https://doi.org/10.1109/mcom.2016.7588228
Patel, P.; Ali, M.; Sheth, A.: From raw data to smart manufacturing: ai and semantic web of things for industry 4.0. IEEE Intell. Syst. 33(4), 79–86 (2018). https://doi.org/10.1109/mis.2018.043741325
Posada, J.; Zorrilla, M.; Dominguez, A.; Simoes, B.; Eisert, P.; Stricker, D.; Rambach, J.; Dollner, J.; Guevara, M.: Graphics and Media Technologies for Operators in Industry 40. IEEE Comput. Graph. Appl. 38(5), 119–132 (2018). https://doi.org/10.1109/mcom.2016.7588228
Lin, C.-C.; Yang, J.-W.: Cost-efficient deployment of fog computing systems at logistics centers in industry 4.0. IEEE Trans. Ind. Inf. 14(10), 4603–4611 (2018). https://doi.org/10.1109/tii.2018.2827920
Yu, B.; Xu, G.; Huang, Z.Z.; Guo, J.G.; Li, Z.; Li, D.Y.; Wang, S.B.; Meng, E.C.; Pan, W.D.; Niu, J.F.; Xue, J.S.; Zhao, T.L.: Theory and its key technology framework of intelligentized fully-mechanized caving mining in extremely thick coal seam. J. China Coal Soc. 44(1), 42–53 (2019). https://doi.org/10.13225/j.cnki.jccs.2018.5050
Si, L.; Wang, Z.B.; Liu, X.H.; Tan, C.; Xu, J.; Zheng, K.H.: Multi-sensor data fusion identification for shearer cutting conditions based on parallel quasi-Newton neural networks and the Dempster-Shafer theory. Sensors 15(11), 28772–28795 (2015). https://doi.org/10.3390/s151128772
Xu, J.; Wang, Z.B.; Tan, C.; Si, L.; Liu, X.H.: A cutting pattern recognition method for shearers based on improved ensemble empirical mode decomposition and a probabilistic neural network [J]. Sensors 15(11), 27721–27737 (2015). https://doi.org/10.3390/s151127721
Ren, F.; Yang, Z.J.; Xiong, S.B.: Study on the coal-rock interface recognition method based on multi-sensor data fusion technique. Chin. J. Mech. Eng. 16(3), 321–324 (2003). https://doi.org/10.3901/CJME.2003.03.321
Tian, L.Y.; Mao, J.; Wang, Q.M.: Coal and rock identification method based on the force of idler shaft in shearer’s ranging arm. J. China Coal Soc. 41(3), 782–787 (2016). https://doi.org/10.13225/j.cnki.jccs.2015.0576
Zhang, N.; Liu, C.: Radiation characteristics of natural gamma-ray from coal and gangue for recognition in top coal caving. Sci. Rep. 8(1), 190 (2018). https://doi.org/10.1038/s41598-017-18625-y
Wang, W.; Zhang, C.: Separating coal and gangue using three-dimensional laser scanning. Int. J. Miner. Process. 694, 79–84 (2017). https://doi.org/10.1016/j.minpro.2017.10.010
Chen, C.: Study on the recognition of coal and rock cutting state and the control strategy of shearer. School of electrical and control engineering, Xi’an University of Science and Technology. Master’s thesis. Xi’an, China, (2019)
Wang, Z.; Si, L.; Tan, C.; Liu, X.: A novel approach for shearer cutting load identification through integration of improved particle swarm optimization and wavelet neural network. Adv. Mech. Eng. (2014). https://doi.org/10.1155/2014/521629
Si, L.; Wang, Z.; Liu, X.; Tan, C.; Zhang, L.: Cutting state diagnosis for shearer through the vibration of rocker transmission part with an improved probabilistic neural network. Sensors 16(4), 479 (2016). https://doi.org/10.3390/s16040479
Xu, J.; Wang, Z.B.; Tan, C.; Si, L.; Liu, X.H.: Cutting pattern identification for coal mining shearer through a swarm intelligence–based variable translation wavelet neural network. Sensors 18(2), 382 (2018). https://doi.org/10.3390/s16040479
Huang, S.J.: The coal-rock interface recognition research based on the digital image processing and clustering technology, Doctor thesis, School of Mechanical Electronic & Information Engineering, China University of Mining & Technology (Beijing). Beijing, China (2016)
Wang, X.: Study of Coal-Rock Identification Method Based on Electromagnetic Wave Technology. Doctor’s thesis. School of Information and Control Engineering, China University of Mining & Technology, Jiangsu Sheng, China, (2017)
Sun J.; Su B.: Coal − rock interface detection using digital image analysis technique. In: Wang X.; Wang F.; Zhong S. (eds) Electrical, Information Engineering and Mechatronics 2011. Lecture Notes in Electrical Engineering, vol 138. Springer, London. (2012). https://doi.org/10.1007/978-1-4471-2467-2_144
Zhang, W.; Li, G. H.; Hu, B.: The study of coal-rock boundary identification and height adjustment of Miner drum based on multi-parameter[C]//International Conference on Information Science & Engineering. IEEE, (2011). https://doi.org/10.1109/icise.2010.5691627
Wang, H.J.: Theoretical and Experimental Study on Coal-rock Interface Identification Based on Multi Information Fusion. Doctor’s thesis. School of Mechanical Engineering, Liaoning Technical University, 2017, Liaoning, China
Tian, L.Y.; Dai, B.H.; Wang, Q.M.: Coal-rock identification method based on multi-strain data fusion of shearer rocker pin shaft. J. China Coal Soc. (2019). https://doi.org/10.13225/j.cnki.jccs.2019.0364
Wei, H.: Identification of coal and gangue by feed-forward neural network based on data analysis. Int. J. Coal Preparation Utilization 39(1), 33–43 (2019). https://doi.org/10.1080/19392699.2017.1290609
Wang, J.C.; Li, L.H.; Yang, S.L.: Experimental study on gray and texture features extraction of coal and gangue image under different illuminance. J. China Coal Soc. 43(11), 3051–3061 (2018). https://doi.org/10.13225/j.cnki.jccs.2018.0866
Sun, J.P.; Su, B.: Coal–rock interface detection on the basis of image texture features. Int. J. Min. Sci. Technol. 23(05), 681–687 (2013). https://doi.org/10.1016/j.ijmst.2013.08.011
Dou, D.Y.; Zhou, D.Y.; Yang, J.G.; Zhang, Y.: Coal and gangue recognition under four operating conditions by using image analysis and Relief-SVM. Int. J. Coal Preparation Utiliz. (2018). https://doi.org/10.1080/19392699.2018.1540416
Liu, F.Q.; Qian, J.S.; Wang, X.H.; Song, J.L.: Automatic separation of waste rock in coal mine based on image procession and recognition. J. China Coal Soc. 25(5), 534–537 (2000). https://doi.org/10.13225/j.cnki.jccs.2000.05.020
Li, L.; Wang, H.; An, L.: Research on recognition of coal and gangue based on image processing. World J. Eng. 12(3), 247–254 (2015). https://doi.org/10.1260/1708-5284.12.3.247
Yu, L.; Zheng, L.X.; Du, Y.Z.; Huang, X.: Image recognition method of coal and coal gangue based on partial grayscale compression extended coexistence matrix. J. Huaqiao Univ. Nat. Sci. 39(6), 906–912 (2018). https://doi.org/10.11830/ISSN.1000-5013.201610012
Liu, K.: The Study on Image Recognition of Coal and Gangue Boundary Signatures Based on the Fractional Calculus. Doctor’s thesis. School of mechanical electronic & information engineering, China University of Mining & Technology (Beijing). Beijing, China, (2018)
Liu, W.; He, K.; Liu, C.Y.; Gao, Q.; Yan, Y.H.: Coal-gangue interface detection based on Hilbert spectral analysis of vibrations due to rock impacts on a longwall mining machine[J]. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 229(8), 1523–1531 (2015). https://doi.org/10.1177/0954406214543409
Song, Q.-j.; Jiang, H.-y.; Zhao, X.; Li, D.-m.: An automatic decision approach to coal–rock recognition in top coal caving based on MF-Score. Pattern Anal. Appl. 20(4), 1307–1315 (2017). https://doi.org/10.1007/s10044-017-0618-7
Song, Q.; Jiang, H.; Song, Q.; Zhao, X.; Wu, X.: Combination of minimum enclosing balls classifier with SVM in coal-rock recognition. PLoS ONE 12(9), e0184834 (2017). https://doi.org/10.1371/journal.pone.0184834
Zhang, G.; Wang, Z.; Zhao, L.: Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders. J. VibroEng. 18(7), 4261–4275 (2016). https://doi.org/10.21595/jve.2016.17386
Yang, Y.; Zeng, Q.; Wan, L.: Dynamic response analysis of the vertical elastic impact of the spherical rock on the metal plate. Int. J. Solids Struct. 158, 287–302 (2019). https://doi.org/10.1016/j.ijsolstr.2018.09.017
Yang, Y.; Zeng, Q.L.; Yin, G.J.; Wan, L.R.: Vibration test of single coal gangue particle directly impacting the metal plate and the study of coal gangue recognition based on vibration signal and stacking integration. IEEE Access. 7, 106784–106805 (2019). https://doi.org/10.1109/ACCESS.2019.2932118
Axelberg, P.G.V.; Gu, Y.H.; Bollen, M.H.J.: Support vector machine for classification of voltage disturbances. IEEE Trans. Power Deliv. (2007). https://doi.org/10.1109/tpwrd.2007.900065
Erfani, S.M.; Rajasegarar, S.; Karunasekera, S.; Leckie, C.: ‘High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning’. Pattern Recognit. 58, 121–134 (2016). https://doi.org/10.1016/j.patcog.2016.03.028
Pandarakone, S.E.; Mizuno, Y.; Nakamura, H.: Distinct fault analysis of induction motor bearing using frequency spectrum determination and support vector machine. IEEE Trans. Ind. Appl. 53(3), 3049–3056 (2017). https://doi.org/10.1109/TIA.2016.2639453
Jan, S.U.; Lee, Y.D.; Shin, J.; Koo, I.: Sensor fault classification based on support vector machine and statistical time-domain features. IEEE Access 5, 8682–8690 (2017). https://doi.org/10.1109/ACCESS.2017.2705644
Zhang, X.; Liang, Y.; Zhou, J.: A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM’. Measurement 69, 164–179 (2015). https://doi.org/10.1016/j.measurement.2015.03.017
Park, Y.W.; Baskiyar, S.: Adaptive scheduling on heterogeneous systems using support vector machine. Computing 99(4), 405–425 (2017). https://doi.org/10.1007/s00607-016-0513-x
Bae, K.Y.; Jang, H.S.; Sung, D.K.: Hourly solar irradiance prediction based on support vector machine and its error analysis. IEEE Trans. Power Syst. 32(2), 935–945 (2017). https://doi.org/10.1109/TPWRS.2016.2569608
Saad, O.M.; Shalaby, A.; Sayed, M.S.: Automatic discrimination of earthquakes and quarry blasts using wavelet filter bank and support vector machine. J. Seismolog. (2018). https://doi.org/10.1007/s10950-018-9810-5
Wang, Y.; Fu, J.; Pan, W.D.: Impact of setting margin on margin setting algorithm and support vector machine. J. Imag. Sci. Technol. 62(3), 1 (2018). https://doi.org/10.2352/j.imagingsci.technol.2018.62.3.030501
Zeng, J.; Lu, D.; Zhao, Y.; Zhang, Z.; Qiao, W.; Gong, X.: Wind turbine fault detection and isolation using support vector machine and a residual-based method. Proc. Amer. Control Conf. (2013). https://doi.org/10.1109/acc.2013.6580398
Hackeling, G. (Write); Zhang, H.R. (Translate): Mastering Machine Learning with scikit-learn Second Edition. Posts and Telecom Press. Beijing, China, (2019)
Acknowledgements
This work was supported by National Natural Science Fund of China (Grant Nos. 51674155, 51974170), Innovative Team Development Project of Ministry of Education (Grant No. IRT_16R45).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author(s) confirm that this article content has no conflicts of interest.
Rights and permissions
About this article
Cite this article
Yang, Y., Zeng, Q. Multipoint Acceleration Information Acquisition of the Impact Experiments Between Coal Gangue and the Metal Plate and Coal Gangue Recognition Based on SVM and Serial Splicing Data. Arab J Sci Eng 46, 2749–2768 (2021). https://doi.org/10.1007/s13369-020-05227-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-020-05227-6