Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine

Abstract

A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares (FLS)-support vector machine (SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.

Keywords

rock and soil / fuzzy theory / vibration excavation / least squares-support vector machine / identification

References

[1] GottliebL. Vibratory cutting of brown coal [J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 1981, 18(4): 335-339

[2] JingY-changResearch of vibration coal cutting mechanism [D], 2000, Beijing, China University of Mining & Technology

[3] MuroT, TranD T. Regression analysis of the characteristics of vibrocutting blade for tuffaceous rock [J]. Journal of Terramechanics, 2004, 40(3): 191-219

[4] ZhaoW-m, ZhouX-b, LuN-l, LiY-shen. Basic research of digging rock and soil with vibration [J]. Construction Machinery, 200056-60

[5] GraffK F. Application of sonic power to rock cutting [J]. Gas Engineering and Management, 1973, 24(6): 28-33

[6] ZhuJ-xinStudy on vibratory excavation mechanism, process optimization modeling and intelligent control strategy of hydraulic excavator [D], 2008, Changsha, Central South University

[7] WangX-d, WangJ-qin. Research and application of support vector machine [J]. Journal of Air Force Engineering University: Natural Science Edition, 2004, 5(3): 49-55

[8] EJ-qiangIntelligent fault diagnosis and its application [M], 2006, Changsha, Hunan University Press: 110-130

[9] SangH-f, WangF-l, HeD-k, ZhangD-peng. Hybrid modeling of fermentation process based on least square support vector machines [J]. Chinese Journal of Scientific Instrument, 2006, 27(6): 629-633

[10] LinC F, WangS D. Fuzzy support vector machines [J]. IEEE Transactions on Neural Networks, 2002, 13(2): 464-471

[11] JiangX F, YiZ, LvJ C. Fuzzy SVM with a new fuzzy membership function [J]. Neural Computing and Application, 2006, 15: 268-276

[12] LeskiJ. M. An epsiv margin nonlinear classifier based on fuzzy if-then rules [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(1): 68-76

[13] ZhangY, SuH-y, ChuJian. Soft sensor modeling based on fuzzy least squares support vector machines [J]. Control and Decision, 2005, 206: 621-624

[14] WangY Q, WangS Y, LaikK. A new fuzzy support vector machine to evaluate credit risk [J]. IEEE Transactions on Fuzzy Systems, 2005, 13(6): 820-831

[15] LiuC, SunD-shan. Determination method of membership of fuzzy SVM [J]. Computer Engineering and Applications, 2008, 44(11): 41-42

[16] ZhangX, XiaoX-l, XuG-you. Determination and analysis of fuzzy membership for SVM [J]. Journal of Image and Graphics, 2006, 11(8): 1188-1192

[17] ZhangQ-y, JieY, LiKai. Method of membership determination for fuzzy support vector machine [J]. Journal of Lanzhou University of Technology, 2009, 35(4): 89-93