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

doi:10.23940/ijpe.19.03.p10.813821

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (3): 813-821.doi: 10.23940/ijpe.19.03.p10.813821

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

Xinliang Wang^{a, b, *}, Zhigang Guo^c, Jianlin Chen^a, Na Liu^d, and Wei Fang^d

a School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China;
b Hami Yuxin Energy Industry Research Institute Co., Ltd., Hami, 839000, China;
c School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China;
d Hami Vocational and Technical College, Hami, 839000, China

Submitted on ; Revised on ;
Contact: junci158@163.com
About author:Xinliang Wang is an associate professor in the School of Physics and Electronic Information Engineering at Henan Polytechnic University. His current research interests include smart power grids, cloud computing, and big data.Zhigang Guo is a graduate student in the School of Electrical Engineering and Automation at Henan Polytechnic University. His research interests include smart power grids, cloud computing, and big data. Weiguo Li works in the Pingmei Shenma Group Equipment Leasing Branch. His research interests include cloud computing and big data.Jianlin Chen is a student in the School of Physics and Electronic Information Engineering at Henan Polytechnic University.Na Liu works at Hami Vocational and Technical College. Her research interests include cloud computing and big data.Wei Fang works at Hami Vocational and Technical College. His research interests include smart power grids, cloud computing, and big data.Xuebin Liu works at Hami Vocational and Technical College. His research interests include smart power grids, cloud computing, and big data.Jun Wu works in the School of Physics and Electronic Information Engineering at Henan Polytechnic University. His current research interests include smart power grids, cloud computing, and big data.

Abstract

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.

Key words: ferrography images analysis, feature extraction, maximum wear particle, C-SVC classifier

Xinliang Wang, Zhigang Guo, Jianlin Chen, Na Liu, and Wei Fang. Detection Algorithm of Friction and Wear State of Large Mechanical and Electrical Equipment in Coal Mine based on C-SVC [J]. Int J Performability Eng, 2019, 15(3): 813-821.

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References

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

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