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

Abstract

To solve the problem that it is difficult to identify the cutting rock wall hardness of the roadheader in coal mine, a recognition method of cutting rock wall hardness is proposed based on multi-source data fusion and optimized probabilistic neural network. In this method, all kinds of cutting signals (the vibration signal of cutting arm, the pressure signal of hydraulic cylinders and current signal of cutting motor) are analyzed by wavelet packet to extract the feature vector, and the multi feature signal sample database of rock cutting with different hardness is established. To solve the problems of uncertain spread and complex network structure of probabilistic neural network (PNN), a PNN optimization method based on differential evolution algorithm (DE) and QR decomposition was proposed, and the rock hardness was identified based on multi-source data fusion by optimizing PNN. Then, based on the ground test monitoring data of a heavy longitudinal roadheader, the method is applied to recognize the cutting rock hardness, and compared with other common pattern recognition methods. The experimental results show that the cutting rock hardness recognition based on multi-source data fusion and optimized PNN has higher recognition accuracy, and the overall recognition error is reduced to 6.8%. The recognition of random cutting rock hardness is highly close to the actual. The method provides theoretical basis and technical premise for realizing automatic and intelligent cutting of heading face.

Dual-carbon data fusion cleaning model based on Kafka cluster

“…As shown in Table 1, in this paper, the attribute of double-carbon data is set to m and the number of operations is n, and the attribute weight of data set J after the I-th data fusion is represented by Y 11 . (Y ma ,Y mb ) is expressed as the attribute value range [7][8][9] of each attribute of the data set after n data cleaning operations. When the attribute value is text data, it is transformed into numerical value through unicode encoding, thus calculating the attribute value domain.…”

Cutting Mechanism Rotor System Dynamic Characteristics of Cantilever Roadheader under Random Hard Rock Load

“…where, Z max [n] is the average peak value of cutting resistance of the standard pick, Y max [n] is the average peak value of traction resistance, k CZ is the influence coefficient of pick arrangement, k bZ is the width coefficient of the cut section in the z-direction, k bY is the width coefficient of the cut section in the y-direction, k YZ is the influence coefficient of cutting angle, k ϕZ is the front edge shape coefficient in the z-direction, k ϕY is the front edge shape coefficient in the y-direction, k YK is the shape coefficient of cutting edge, and k X is the ratio of lateral resistance to cutting resistance [19][20][21].…”

Application of an improved naive Bayesian analysis for the identification of air leaks in boreholes in coal mines

“…Following MDF theory, the training samples for gas drainage borehole leaks are processed at the data level 33 , and the processed data are standardized with Eqs. ( 2 )–( 4 ).…”