Cutting performance evaluation of a roadheader machine by PCA and RBF

Abstract

As coal mining technology has continuously evolved, gradually the industry has moved toward fully mechanized mining. A roadheader machine is important mechanical equipment for roadway drivage through mechanical crushing. Through analysis and research to discover the key parameters relating to the cutting performance of the roadheader machine, the performance of the roadheader machine must be optimized and costs reduced, as well as productivity increased. As one of the most important statistical methods, principal component analysis (PCA) could not only reduce many factors into fewer overall targets, it could also provide the comparative item weighting, improving the computational efficiency and error precision of the radial basis function neural network, eliminating the correlation of each input variable, and increasing the stability of the network model. The principal variables are determined and a cutting performance evaluation model developed that allow both performance prediction and cutting performance evaluation. From the analysis it is concluded that the primary indicators of the roadheader cutting performance were the unidirectional compressive strength, the cutting resistance fluctuation, the weaving speed of the cutting head, the cutting power fluctuation, and the traction resistance fluctuation. The model is consistent with the practical test results and contributes to discovery of future optimization procedures.

Rock hardness identification based on optimized PNN and multi-source data fusion

“…7 In recent years, many studies, which aimed to increase cutting performance and reduce cutting consumption by using the roadheaders more efficiently, have been conducted by different researchers. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Ramazan comakli et al deeply analyzed the influences of coal and rock properties on roadheader cutting load, and subjected different sandstones with uniaxial compressive strength to cutting tests under different levels of cutting parameters, and the change rule of roadheader cutting load under different coal rock properties is obtained. [8][9][10] K. Fukui et al suggested a method to estimate rock strength from the cutting force exerted by the roadheader, based on the results of laboratory experiments, which offers a basis for selection of cutting head with different geological conditions of coal or rock.…”

“…[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Ramazan comakli et al deeply analyzed the influences of coal and rock properties on roadheader cutting load, and subjected different sandstones with uniaxial compressive strength to cutting tests under different levels of cutting parameters, and the change rule of roadheader cutting load under different coal rock properties is obtained. [8][9][10] K. Fukui et al suggested a method to estimate rock strength from the cutting force exerted by the roadheader, based on the results of laboratory experiments, which offers a basis for selection of cutting head with different geological conditions of coal or rock. [11][12][13] Sadi Evren Seker et al used different machine learning algorithms and a combination of various machine learning algorithms via ensemble techniques to predict roadheader performance, and obtained the main indexes affecting roadheader performance, so as to optimize roadheader parameters and improve roadheader cutting performance.…”

Recognition of cutting rock hardness by hydraulic cylinder pressure

“…5 In recent years, many studies, which aimed to increase cutting performance and reduce cutting consumption by using the roadheaders more efficiently, have been conducted by different researchers. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] Ramazan Comakli et al deeply analyzed the influences of coal and rock properties on roadheader cutting load and subjected different sandstones with uniaxial compressive strength to cutting tests under different levels of cutting parameters, and the change rule of roadheader cutting load under different coal rock properties is obtained. [6][7][8][9] H. Ergin et al established the mathematical model of the cutting system of longitudinal roadheader, and they obtained the threedimensional force of the cutting head.…”

“…12 Sadi Evren Seker et al used different machine learning algorithms and a combination of various machine learning algorithms via ensemble techniques to predict roadheader performance, and obtained the main indexes affecting roadheader performance, so as to optimize roadheader parameters and improve roadheader cutting performance. [13][14][15][16][17][18] Philipp Hartlieb and Stefan Bock constructed the overall dynamic model of the roadheader based on the Lagrangian principle. The cutting head parameters are optimized and the cutting efficiency of the roadheader is improved by analyzing dynamic characteristics of roadheader cutting system.…”

Multisensor information-based adaptive control method for cutting head speed of roadheader

“…In Jasiulek and Świder, 28 an artificial neural network was used to determine the cutting resistance to set the angular velocity of a cutting jib on a plane parallel to the roadway’s floor. In Zhao and Wang, 29 principal component analysis and a radial basis function neural network were used to evaluate the cutting performance of a roadheader. Herein, it was demonstrated that multisensor information significantly affected the cutting performance of the roadheader.…”