基于EMD与神经网络的煤岩界面识别方法  

Coal-Rock Interface Recognition Method Based on EMD and Neural Network

作　　者：王保平[1] 王增才[1] 张万枝[1] 

机构地区：[1]山东大学机械工程学院,济南250061

出　　处：《振动．测试与诊断》2012年第4期586-590,688,共5页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(编号:51174126)

摘　　要：针对放顶煤中煤岩界面难以识别的问题,采用检测液压支架尾梁振动信号的方式进行了研究,提出一种基于经验模态分解(empirical mode decomposition,简称EMD)和神经网络的识别方法。首先,对振动信号进行经验模态分解,得到多个固有模态分量(intrinsic mode functions,简称IMF);然后,对各IMF进一步分析以提取特征参数;最后,选择若干个包含主要信息的参数组成特征向量作为神经网络的输入来识别落煤和落岩两种情况,实现煤岩界面的自动识别。实验结果表明,分别以各IMF的能量、峭度和波峰因子组成的特征向量均可用于识别煤岩界面,且当以能量组成特征向量时比其他两种方式具有更高的识别率。In order to recognize the coal-rock interface in the top coal caving, the vibration signals of the tail beam of the hydraulic support are investigated. A new method based on empirical mode decomposition (EMD) and neural network is proposed. Firstly, EMD is employed to decompose the original signal into a number of stationary intrinsic mode functions (IMF). Subsequently, a further analysis of the IMF is chosen to extract the characteristic parameters. Finally, the parameters that contained the main information compose feature vectors and are chosen as the input of the neural network to identify the coal-rock interface. The experimental result shows that kurtosis and crest factor of the IMFs can all be used to identify the coal-rock interface and a higher identification rate is achieved using energy than the other two methods.

关 键 词：经验模态分解 神经网络 煤岩界面识别 综放开采 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TH87[自动化与计算机技术—计算机科学与技术]