基于小波包能量流和LTSA的垮落煤岩特征提取

Feature extraction by wavelet packet energy flow and LTSA in collapsing coal-rock recognition

针对综放工作面垮落煤岩识别的技术问题,采集了放煤过程中垮落煤岩冲击液压支架后尾梁的振动信号,并提出了一种基于小波包能量流和LTSA的特征提取方法。该方法首先利用小波包变换把振动信号分解成一系列的时频子空间;为了观察原信号能量在各层时频子空间的分布特征,计算了小波包分解每一层各个时频子空间的能量,构成了一个小波包能量矩阵,称为小波包能量流;然后利用局部切空间排列(Local Tangent Space Alignment,LTSA)挖掘小波包能量流的低维流形。为了验证小波包能量流低维流形的有效性,把该特征向量输入BP神经网络来识别垮落煤岩。结果表明:基于小波包能量流和LTSA提取的特征向量可以准确简约地表征垮落煤岩,BP神经网络的识别率达到100%。

In order to recognize the collapsing coal and rock,the vibration signals caused by the impact of collapsing coal-rock and hydraulic support tail beam are collected at the scene of a fully mechanized caving face.Then a new feature extraction method based on wavelet packet energy flow and LTSA is proposed,which is achieved by three main steps： firstly,the wavelet packet transform is conducted to decompose the vibration signals into a set of different timefrequency subspaces; then the wavelet packet energy flow is formed through time-frequency subspaces from low layer to high layer,the energy is calculated in each subspace of each layer; finally,low-dimensional manifold features carrying class information are extracted from the wavelet packet energy flow by using the LTSA algorithm.To verify the effectiveness of the low-dimensional manifold feature,it is used as the input of BP neural network to identify the collapsing coal and rock.The experimental results show that the proposed feature based on wavelet packet energy flow and LTSA is both accurate and concise,and the neural network identification rate reaches 100%.