Abstract
Accurate recognition of coal-rock cutting state is a prerequisite for intelligent operation of shearer, so as to achieve safe and efficient production in coal mines. This paper takes the sound signal, Y-axis and Z-axis vibration signals as analytic objects and proposes a fusion recognition method for shearer coal-rock cutting state via the combination of improved radical basis function neural network (RBFNN) and Dempster-Shafer (D-S) evidence theory. First of all, on the basis of original fruit fly optimization algorithm (FOA), the location updating mechanism of moth-flame optimization (MFO) is used to improve the convergence performance and exploration ability of FOA. Thus, a hybrid optimization algorithm of MFO-FOA is accordingly designed and some simulations are conducted to verify the effectiveness and superiority. Then, the optimal network parameters of RBFNN are found out by using proposed MFO-FOA to realize the excellent generalization ability and predictive performance. Moreover, the collected signals are decomposed by variational mode decomposition, and the envelope entropy and kurtosis are used to extract the features of first three intrinsic mode function components. The feature vectors obtained from three-type sensor data are utilized to construct the RBFNN classifiers. Besides, the D-S evidence theory with evidence correlation coefficient is introduced to fuse the preliminary identification results of three RBFNN classifiers. Finally, a self-designed experimental platform for shearer cutting coal-rock is built and some experiments are provided. The experimental results based on measured data demonstrate that the proposed method can effectively identify the coal-rock cutting state with higher accuracy.
Highlights
Coal is the most abundant and widely distributed fossil fuel on the earth
The major contributions of the proposed recognition scheme can be summarized as follows: (1) we propose a new optimization algorithm based on the combination of moth-flame optimization (MFO) and fly optimization algorithm (FOA) to find out the optimal network parameters of radical basis function neural network (RBFNN)
In this study, the sound signals and vibration signals both are taken as analytic objects, and a novel method for coalrock cutting state recognition of shearer based on improved RBFNN and D-S evidence theory is proposed
Summary
Coal is the most abundant and widely distributed fossil fuel on the earth. In China’s proven fossil energy, coal accounts for about 94 percent. L. Si et al.: Fusion Recognition of Shearer Coal-Rock Cutting State Based on Improved RBFNN and D-S Evidence Theory coal mine in Pennsylvania, USA. The memory cutting method can improve the automatic control level of shearer to a certain extent, the application effect is not ideal when the coal seam breaks suddenly Under this background, some scholars try to identify the coal-rock cutting state of shearer to provide the basis for its intelligent control. The single-type sensor data are not reliable enough in actual working condition, and will reduce the recognition effect of shearer coal-rock cutting state based on the proposed RBFNN model-based classifier. (2) A new recognition technique based RBFNN optimized by using MFO-FOA is presented to achieve the coal-rock cutting state recognition based on three-type sensing data collected from one sound sensor and two vibration sensors. Where ωij is the connection weight between the hidden layer and the output layer, h is the number of nodes in the hidden layer, and θj is the threshold value of the j-th output node, which is the actual output value of the j-th output node in the output layer
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