Coal and gangue identification method based on EMD feature extraction and random forest
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摘要: 基于振动信号辨识是实现综放开采煤矸识别的有效手段，现有方法在识别准确性和有效性方面有待进一步研究。提出了一种基于经验模态分解（EMD）特征提取与随机森林（RF）的煤矸识别方法。采用加速度传感器及数据采集仪采集了某综放工作面煤和矸石冲击液压支架尾梁产生的振动信号，分别对2种信号进行EMD，得到一系列本征模态函数（IMF）；根据EMD结果选取有效IMF，分别提取IMF能量、峭度、矩阵奇异值及对应熵作为特征向量，采用各特征向量独立训练RF模型，根据各RF模型对测试样本的识别结果筛选特征向量，并建立特征数据集；采用特征数据集训练RF模型，采用训练好的RF模型实现煤矸识别。测试结果表明：该方法对200组煤矸测试样本的识别准确率达96.5%，且当RF模型中决策树数量设置为100或150时识别准确率最高，对测试样本进行特征提取与识别的耗时不超过0.2 s，满足综放工作面煤矸识别准确性和实时性要求。Abstract: Identification based on vibration signals is an effective method to realize coal and gangue identification in fully mechanized mining. The existing method needs to be further studied in terms of identification accuracy and effectiveness. A coal and gangue identification method based on empirical mode decomposition(EMD) feature extraction and random forest(RF) is proposed in this study. The acceleration sensor and data acquisition instrument are used to collect the vibration signals generated by the impact of coal and gangue on the tail beam of the hydraulic support in a fully mechanized working face. Then the two signals are processed by EMD respectively so as to obtain a series of intrinsic mode functions(IMF). The effective IMFs are selected according to the EMD results, and the IMF energy, kurtosis, matrix singular values and corresponding entropy are extracted as feature vectors. Each feature vector is used to train the RF model independently. The feature vectors are filtered according to the identification results of each RF model on the test samples, and the feature data set is established. The feature data set is used to train the RF model, and the trained RF model is applied to realize the coal and gangue identification. The test results show that the identification accuracy of the method reaches 96.5% for 200 sets of coal and gangue test samples, and the highest identification accuracy is achieved when the number of decision trees in the RF model is set to 100 or 150. Furthermore, the time consumed for feature extraction and identification of test samples is less than 0.2 s, which meets the requirements of accuracy and real time of coal and gangue identification in fully mechanized working face.
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期刊类型引用(7)
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