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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)


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.


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