多通道信息融合煤矸识别方法研究  

Research on Coal and Gangue Identification Method with Multi-Channel Information Fusion

作　　者：孟彬 张子鹏 吴明珂 王瑶 杨善国[1,2,3] MENG Bin;ZHANG Zi-peng;WU Ming-ke;WANG Yao;YANG Shan-guo(School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116;Jiangsu Province and Education Ministry Co-sponsored Collaborative Innovation Center of Intelligent Mining Equipment,Xuzhou 221116;State Key Laboratory of Intelligent Mining Equipment and Technology,Xuzhou 221116)

机构地区：[1]中国矿业大学机电工程学院,江苏徐州221116 [2]江苏省矿山智能采掘装备协同创新中心,江苏徐州221116 [3]智能采矿装备技术全国重点实验室,江苏徐州221116

出　　处：《制造业自动化》2025年第4期48-53,共6页Manufacturing Automation

基　　金：国家自然科学基金(52274162);江苏高校优势学科建设工程资助项目(PAPD)。

摘　　要：为提高放顶煤过程中煤矸识别准确率,提出了一种结合变分模态分解(VMD)、主成分分析(PCA)和卷积神经网络(CNN)的多通道信息融合煤矸识别方法。首先,搭建放顶煤液压支架煤矸滑移试验台,采集多个传感器通道的煤矸振动数据;其次,将采集到的数据利用VMD分解得到本征模态函数(IMF),分别计算每个IMF分量的时域和频域特征,接着将特征进行PCA降维,得到降维后的煤矸振动信号特征向量并以此构建煤矸振动信号特征数据集;然后,利用CNN模型分别对不同通道数据进行训练;最后,通过加权平均法进行多通道信息融合来综合评判识别。研究结果表明该方法具有较高的煤矸识别准确率,经信息融合后准确率可达97.15%,煤矸识别效果良好。To improve the accuracy of coal and gangue identification in the process of top coal caving,a multichannel information fusion method for coal gangue identification combining variational mode decomposition(VMD),principal component analysis(PCA)and convolutional neural network(CNN)is proposed.Firstly,the coal gangue slip test bench of caving hydraulic support is built,and the vibration data of coal gangue in multiple sensor channels are collected.Secondly,the collected data are decomposed into intrinsic mode function(IMF)using VMD,and the time domain and frequency domain features of each IMF component are calculated respectively.Then,the features are reduced by PCA to obtain the feature vector of coal gangue vibration signal after dimension reduction and construct the feature data set of coal gangue vibration signal.Afterwards,the CNN model is used to train different channel data respectively.Finally,the weighted average method is used for multi-channel information fusion to conduct comprehensive evaluation and identification.The results show that the method has a high accuracy of coal gangue identification,the accuracy rate can reach 97.15%after information fusion,and the coal and gangue identification effect is good.

关 键 词：放顶煤 煤矸识别 特征向量 信息融合 

分 类 号：TD823[矿业工程—煤矿开采] TP391.4[矿业工程—矿山开采]