Research on the prediction model of drilling anti-jamming valve based on GA-BPNN

Abstract Anti-jamming system has been widely used in rock drill rigs, in order to avoid uncontrolled jamming of the bit. An anti-jamming valve is the core component of the anti-jamming system. The backpropagation neural network (BPNN) was used in this study to construct an estimating model for the prediction of feed pressure, so as to find the key factors of jamming problems. Because the parameters of BPNN have a significant influence on results, and genetic algorithm (GA) is capable of quickly finding optimal solutions. We integrated GA and BPNN so that the convergence rate was improved and precision was relatively enhanced. The results showed that, BPNN realised a reasonable prediction for the feed pressure of anti-jamming valve. Moreover, when the parameters for GA are set between reasonable ranges, GA optimisation for such neural network is feasible, and the obtained results are satisfying, which has relatively higher accuracy of prediction, non-linear mapping and better network performance.

[1]  Marian Wiercigroch,et al.  Modelling of high frequency vibro-impact drilling , 2015 .

[2]  J. C. Dai,et al.  Structural parameters multi-objective optimisation and dynamic characteristics analysis of large-scale wind turbine towers , 2018 .

[3]  Tao Jiang,et al.  Research on the dynamic characteristics of mast mechanism of rotary drilling rig , 2011 .

[4]  RUIXIN LIU,et al.  Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network , 2014, Experimental and therapeutic medicine.

[5]  Ying Xiong.  Neural Network Based on Sum Squared Relative Error to Predict the Multixial Fatigue Life of Magnesium Alloy , 2016 .

[6]  Qingyun Du,et al.  Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China , 2015, Natural Hazards.

[7]  Xiantao Liu,et al.  Prediction of long-term gas load based on particle swarm optimization and gray neural network model , 2017 .

[8]  P. Sreeraj,et al.  Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models , 2012 .

[9]  Tariq Muneer,et al.  Neural network based method for conversion of solar radiation data , 2013 .

[10]  M. Ataei,et al.  Drilling rate prediction of an open pit mine using the rock mass drillability index , 2015 .

[11]  Peng Hao-yue.  The BP neural network's GA optimization and its realization on MATLAB , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).

[12]  F. Şahin,et al.  Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural network , 2015 .

[13]  R. Jeyapaul,et al.  Comparative analysis of delamination factor prediction using RSM and ANN during endmilling of GFRP composites , 2017 .

[14]  Xuewu Cao,et al.  Study of Liquid Lithium Coolant Interaction Based on BP Neural Network Optimized by Genetic Algorithm , 2015 .

[15]  Xinhua Liu,et al.  A Novel Approach for Shearer Cutting Load Identification through Integration of Improved Particle Swarm Optimization and Wavelet Neural Network , 2014 .

[16]  Håkan Schunnesson,et al.  Production control and optimization in open pit mining using a drill monitoring system and an image analysis system : a case study from Aitik copper mine in Sweden , 2009 .

[17]  Chao Li,et al.  Study on high-speed cutting parameters optimization of AlMn1Cu based on neural network and genetic algorithm , 2016 .

[18]  Chunhua Yang,et al.  Dynamic simulation and test research of impact performance of hydraulic rock drill with no constant-pressurized chamber , 2014 .

[19]  R. Jeyapaul,et al.  Modelling of machining force in end milling of GFRP composites using MRA and ANN , 2016 .

[20]  Guijun Wang,et al.  Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm , 2014, International Journal of Machine Learning and Cybernetics.

[21]  Geun-Ho Lee,et al.  Modeling and performance analysis of rock drill drifters for rock stiffness , 2012 .

[22]  Qian Ming.  Analysis on typical anti-jamming system of hydraulic rock drill rigs , 2013 .

[23]  Jun Li,et al.  Combined Percussive-Rotary Drilling to Increase Rate of Penetration and Life of Drill Bit in Drilling Hard Rock Formation , 2017, Chemistry and Technology of Fuels and Oils.

[24]  Subramanian Kartheeswaran,et al.  Minimize the mean square error by data segregation approach for back-propagation artificial neural network with adaptive learning based image reconstruction in electron magnetic resonance imaging tomography , 2015, 2015 Online International Conference on Green Engineering and Technologies (IC-GET).

[25]  Uday Kumar,et al.  The use of specific energy in rotary drilling : the effect of operational parameters , 2015 .

[26]  Belkacem Draoui,et al.  Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions , 2011, International Journal of Biometeorology.

[27]  H. S. Wang,et al.  Cost estimation of plastic injection molding parts through integration of PSO and BP neural network , 2013, Expert Syst. Appl..

[28]  J. Adamowski,et al.  Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada , 2012 .

[30]  Long Yun.  Control strategy of blockage prevention of hydraulic rock drill , 2009 .

[31]  Jaho Seo,et al.  A percussion performance analysis for rock-drill drifter through simulation modeling and experimental validation , 2016 .