基于BP神经网络的煤层硬度多等级识别方法

摘要 针对煤层硬度识别方面存在的问题，提出一种基于BP神经网络算法的煤层硬度多等级识别方法，将煤层硬度划分为6个等级进行识别.以采煤机截割电机和牵引电机的定子电流信号及调高油缸压力信号作为识别信号，利用小波包分解提取各个信号的特征量，并将其作为神经网络的输入样本进行训练和测试.经过实验，在仿真数据条件下本文提出的煤层硬度多等级识别方法对硬度等级的识别准确率为96.7%，在实机数据条件下识别准确率为93.3%，验证了该煤层硬度识别方法的有效性，为采煤机自适应截割过程煤层硬度高精度识别奠定了理论基础.
Abstract：A BP neural network algorithm based hierarchical identification method， which divides the coal seam hardness into six levels， was proposed for identifying the coal seam hardness. The identification signals were taken from the stator currents of both the cutting motor and the traction motor of the mining machine， as well as the pressure signal of the height adjustment cylinder. The wavelet packet decomposition was used for extracting the characteristics of each signal， and these signals were taken as the input signals for training and testing the neural network. The experimental results show that the identification accuracy reaches 96.7% and 93.3% toward the simulation data and the real data， respectively， validating the effectiveness of the method. The method proposed provides the foundation for precisely identification of coal seam hardness.