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Detection Algorithm of Friction and Wear State of Large Mechanical and Electrical Equipment in Coal Mine based on C-SVC

Volume 15, Number 3, March 2019, pp. 813-821
DOI: 10.23940/ijpe.19.03.p10.813821

Xinliang Wanga,b, Zhigang Guoc, Jianlin Chena, Na Liud, and Wei Fangd

aSchool of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China
bHami Yuxin Energy Industry Research Institute Co., Ltd., Hami, 839000, China
cSchool of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China
dHami Vocational and Technical College, Hami, 839000, China

(Submitted on October 21, 2018; Revised on November 24, 2018; Accepted on December 22, 2018)

Abstract: 

The large-scale electromechanical equipment of coal mines has the characteristics of low speed, heavy loads, and complicated operation environment. Existing features, such as shape, color, and texture, are directly used to detect the friction and wear state of large mechanical and electrical equipment in coal mines, and the effect is not satisfactory. In this paper, a multivariate feature extraction algorithm based on maximum wear particles is proposed, and the C-SVC classifier model is constructed based on the extracted features. The simulation results show that compared with SVM (Support Vector Machine) and the decision tree algorithm, the model of C-SVC classifier based on the multiplex feature of the largest block wear particles has better classification accuracy, better generalization ability, and better robustness.

 

References: 15

        1. H. L. Li, “Research on Ferrographic Image Segmentation and Wear Particle Feature Extraction Technology,” Nanjing University of Aeronautics and Astronautics, Nan Jing, 2009
        2. J. L. Kang, Y. P. Lu, and Y. S. Zhou, “Wear Particle Recognition with Improved BP Algorithm,” LUBRICATION ENGINEERING, Vol. 3, pp. 41-42, 2004
        3. R. H. Qiu, H. Zhang, and X. R. Zhang, “Oil Analysis Technique and its Application in Modern Paper Making Machinery Fault Diagnosis,” Transactions of China Pulp and Paper, Vol. 24, No. 3, pp. 121-126, 2009
        4. D. Zhang and P. J. Liang, “Design of the Expert System for Wear Particle Recognition based on Neural Network,” Equipment Manufacturing Technology, Vol. 11, pp. 38-40, 2010
        5. Z. R. He, Z. W. Sun, Z. N. Xuan, and Z. H. Duan, “Fault Diagnosis of the Gearbox of Petrochemical Extrusion Granulation Unit based on Oil Analysis Technology,” Petro-Chemical Equipment Technology, Vol. 37, No. 1, pp. 15-18, 2016
        6. E. Zhang, “Ferrography Technology and its Industrial Application,” Xi’an Jiao Tong University Press, Xi’an, 2001
        7. Q. Li, T. G. Liu, C. Zhang, J. B. Zhao, and H. C. Zhang, “Study on Wear Condition Monitoring of Coal Mine Machinery by Comprehensive Ferrography Analysis Method,” Coal Technology, No. 36, pp. 291-293, 2017
        8. Z. X. Fu, “Compressor Oil Analysis Techniques in Coal Lubrication and Maintenance Management in Use,” Coal Mine Machinery, Vol. 35, No. 4, pp. 183-184, 2014
        9. X. J. Qin, “Application of Modern Oil Analysis Technology in Coal Mine Equipment Management,” Science and Technology Innovation and Application, No. 18, pp. 153-153, 2015
        10. J. R. Huang, “Application of Oil Analysis Technology in Monitoring of Coal Mine Operating Equipment,” Electromechanical Information, No. 18, pp. 84-85, 2015
        11. X. M. Xie and G. H. Liang, “Application of Iron Spectrum Analysis in Large Equipment of Coal Mine,” ENERGY AND ENERGY CONSERVATION, No. 10, pp. 184-185, 2016
        12. V. Vapnik, “The Nature of Statistical Learning Theory,” Springer, New York, pp. 123-167, 2000
        13. F. Wang, “Study on the Ferrography Wear Particle With Image Processing Technology,” Wuhan University of Technology, Wuhan, 2005
        14. L. J. Qiu, “Based on Support Vector Machine Ferrography Image Recognition Technology,” Taiyuan University of Technology, 2015
        15. M. M. Dundar, “A Cost-Effective Semisupervised Classifier Approach with Kernels,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 1, pp. 264-270, 2004

               

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