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Machine vision online detection of ore granularity based on edge computing

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Belts are widely applied in mine production for conveying ores. Understanding ore granularity, which is a crucial factor in determining the effectiveness of crushers, is vital for optimising production efficiency throughout the crushing process and ensuring the success of subsequent operations. Based on edge computing technology, an online detection method is investigated to rapidly and accurately obtain ore granularity information on high-speed conveyor belts. The detection system utilising machine vision technology is designed in this paper. The high-speed camera set above the belt is used to collect the image of the ore flow, and the collected image is input into the edge computing device. After binary, grey morphology and convex hull algorithm processing, the particle size distribution of ore is obtained by statistical analysis. Finally, a 5G router is used to output the settlement result to a cloud platform. In the GUANBAOSHAN mine of Ansteel Group, the deviation between manual screening and image particle size analysis was studied. Experimental results show that the proposed method can detect the ore granularity, ore flow width and ore flow terminal in real-time. It can provide a reference for the staff to adjust the parameters of the crushing equipment, reduce the mechanical loss and the energy consumption of the equipment, improve the efficiency of crushing operation and reduce the failure rate of the crusher.
Rocznik
Strony
335--350
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
autor
  • Northeastern University, China
autor
  • Chinese Academy of Sciences Allwin Technology Co., Ltd, China
autor
  • Chinese Academy of Sciences Allwin Technology Co., Ltd, China
autor
  • Chinese Academy of Sciences Allwin Technology Co., Ltd, China
autor
  • Ansteel Group Guanbaoshan Mining Co., Ltd, China
autor
  • Ansteel Group Guanbaoshan Mining Co., Ltd, China
Bibliografia
  • [1] L. Ma, Y. Zhang, G. Song, Z. Ma, T. Lu, In Ore Granularity Detection and Analysis System Based on Image Processing. 31st Chinese Control And Decision Conference (CCDC). Nanchang, Peoples R. China, Jun 03-05; Nanchang, Peoples R. China, 359-366 (2019). DOI: https://doi.org/10.1109/CCDC.2019.8832862.
  • [2] J. Chen, H. Huang, A.G. Cohn, D. Zhang, M. Zhou, Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning. International Journal Of Mining Science And Technology 32 (2), 309-322 (2022). DOI: https://doi.org/10.1016/j.ijmst.2021.08.004.
  • [3] X. Li, L. Shen, Z. Ming, C. Zhang, H. Jiang, Laser-based on-line machine vision detection for longitudinal rip of conveyor belt. Optik 168, 360-369 (2018). DOI: https://doi.org/10.1016/j.ijleo.2018.04.053.
  • [4] Y.M. Song, H. Ren, H.L. Xu, D. An, Experimental Study on Deformation and Damage Evolution of a Mining Roadway with Weak Layer Rock under Compression-shear Load. Archives of Mining Sciences 66 (3), 351-368 (2021). DOI: https://doi.org/10.24425/ams.2021.138593.
  • [5] J. Zhou, J. Yu, Chisel edge wear measurement of high-speed steel twist drills based on machine vision. Computers In Industry 128, 103436 (2021). DOI: https://doi.org/10.1016/j.compind.2021.103436.
  • [6] M.T. Habib, A. Majumder, A.Z.M. Jakaria, M. Akter, M.S. Uddin, F. Ahmed, Machine vision based papaya disease recognition. Journal Of King Saud University-Computer And Information Sciences 32 (3), 300-309 (2020). DOI: https://doi.org/10.1016/j.jksuci.2018.06.006.
  • [7] Z. Liu, B. Qu, Machine vision based online detection of PCB defect. Microprocessors and Microsystems 82, 103807 (2021). DOI: https://doi.org/10.1016/j.micpro.2020.103807.
  • [8] R.M.A. Eshaq, E. Hu, H.A.A.M. Qaid, Y. Zhang, T. Liu, Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue. IEEE Access 9, 147315-147327 (2021). DOI: https://doi.org/10.1109/ACCESS.2021.3121270.
  • [9] Y. Guo, L. Chai, S.E. Aggrey, A. Oladeinde, J. Johnson, G. Zock, A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution. Sensors 20 (11), 3179 (2020). DOI: https://doi.org/10.3390/s20113179.
  • [10] H. Tian, D. Wang, J. Lin, Q. Chen, Z. Liu, Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision. Sensors 20 (16), 4531 (2020). DOI: https://doi.org/10.3390/s20164531.
  • [11] H.M.A. Rashid, M. Ghazzali, U. Waqas, A.A. Malik, M.Z. Abubakar, Artificial Intelligence-Based Modeling for the Estimation Of Q-Factor and Elastic Young’s Modulus of Sandstones Deteriorated by a Wetting-Drying Cyclic Process. Archives of Mining Sciences 66 (4), 635-658 (2021). DOI: https://doi.org/10.24425/ams.2021.138944.
  • [12] S.K. Baduge, S. Thilakarathna, J.S. Perera, M. Arashpour, P. Sharafi, B. Teodosio, A. Shringi, P. Mendis, Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction 141, 104440 (2022). DOI: https://doi.org/10.1016/j.autcon.2022.104440.
  • [13] C. Bhondayi, Flotation Froth Phase Bubble Size Measurement. Mineral Processing and Extractive Metallurgy Review 43 (2), 251-273 (2022). DOI: https://doi.org/10.1080/08827508.2020.1854250.
  • [14] J. Maitre, K. Bouchard, L.P. Bedard, Mineral grains recognition using computer vision and machine learning. Computers & Geosciences 130, 84-93 (2019). DOI: https://doi.org/10.1016/j.cageo.2019.05.009.
  • [15] Z. Lu, X. Hu, Y. Lu, Particle Morphology Analysis of Biomass Material Based on Improved Image Processing method. International Journal Of Analytical Chemistry 2017 (2017). DOI: https://doi.org/10.1155/2017/5840690.
  • [16] A. Laucka, V. Adaskeviciute, D. Andriukaitis, Research of the Equipment Self-Calibration Methods for Different Shape Fertilizers Particles Distribution by Size Using Image Processing Measurement Method. Symmetry-Basel 11 (7), 838 (2019). DOI: https://doi.org/10.3390/sym11070838.
  • [17] Z. Zhang, J. Yang, Online Analysis of Coal Ash Content on a Moving Conveyor Belt by Machine Vision. International Journal of Coal Preparation and Utilization 37 (2), 100-111 (2017). DOI: https://doi.org/10.1080/19392699.2016.1140650.
  • [18] O.d.F. Martins Gomes, J.C. Alvarez Iglesias, S. Paciornik, M.B. Vieira, Classification of hematite types in iron ores through circularly polarized light microscopy and image analysis. Minerals Engineering 52, 191-197 (2013). DOI: https://doi.org/10.1016/j.mineng.2013.07.019.
  • [19] A. Heyduk, Elliptical shape and size approximation of a particle contour. IOP Conference Series: Earth and Environmental Science 261 (1), 012013 (2019). DOI: https://doi.org/10.1088/1755-1315/261/1/012013.
  • [20] T. Thakur, A. Mehra, V. Hassija, V. Chamola, R. Srinivas, K.K. Gupta, A.P. Singh, Smart water conservation through a machine learning and blockchain-enabled decentralized edge computing network. Applied Soft Computing 106, 107274 (2021). DOI: https://doi.org/10.1016/j.asoc.2021.107274.
  • [21] M. Aazam, S. Zeadally, E.F. Flushing, Task offloading in edge computing for machine learning-based smart healthcare. Computer Networks 191, 108019 (2021). DOI: https://doi.org/10.1016/j.comnet.2021.108019.
  • [22] R. Rajavel, S.K. Ravichandran, K. Harimoorthy, P. Nagappan, K.R. Gobichettipalayam, IoT-based smart healthcare video surveillance system using edge computing. Journal of Ambient Intelligence and Humanized Computing 13(6), 3195-3207 (2022). DOI: https://doi.org/10.1007/s12652-021-03157-1.
  • [23] M. Gawas, H. Patil, S.S. Govekar, An integrative approach for secure data sharing in vehicular edge computing using Blockchain. Peer-To-Peer Networking and Applications 14 (5), 2840-2857 (2021). DOI: https://doi.org/10.1007/s12083-021-01107-4.
  • [24] C. Li, X. Gao, S.L. Rowan, B. Hughes, W.A. Rogers, Measuring binary fluidization of nonspherical and spherical particles using machine learning aided image processing. AIChE Journal 68 (7), e17693 (2022). DOI: https://doi.org/10.1002/aic.17693.
  • [25] X. Jia, Application of Planar Binary Image in Building Elevation Design. Scientific Programming 2022, 9171941 (2022). DOI: https://doi.org/10.1155/2022/9171941.
  • [26] K.T.N. Duarte, M.A.N. Moura, P.S. Martins, M.A.G.d. Carvalho, Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques. IEEE Latin America Transactions 20(5), 831-838 (2022). DOI: https://doi.org/10.1109/TLA.2022.9693568.
  • [27] D.S. Chéles, A.S. Ferreira, I.S. de Jesus, E.I. Fernandez, G.M. Pinheiro, E.A. Dal Molin, W. Alves, R.C.M. de Souza, L. Bori, M. Meseguer, J.C. Rocha, M.F.G. Nogueira, An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction. Applied Sciences 12 (7), 3531 (2022). DOI: https://doi.org/10.3390/app1207353.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9271897c-7b13-4207-8615-8f95cb5969e7
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