基于IMF能量矩和SVM的煤矸识别

窦希杰1,2,王世博1,2,谢洋1,2,宣统1,2

振动与冲击 ›› 2020, Vol. 39 ›› Issue (24) : 39-45.

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (24) : 39-45.
论文

基于IMF能量矩和SVM的煤矸识别

  • 窦希杰1,2,王世博1,2,谢洋1,2,宣统1,2
作者信息 +

Coal and gangue identification based on IMF energy moment and SVM

  • DOU Xijie1,2,WANG Shibo1,2,XIE Yang1,2,XUAN Tong1,2
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文章历史 +

摘要

针对综放工作面的煤矸识别问题,提出了一种基于IMF能量矩和SVM的煤矸识别方法。使用仿真信号验证了该方法所提取的IMF能量矩可以反映信号的能量沿时间轴的分布情况,相较于IMF能量可以更好地表征信号的特征。使用该方法进行煤矸识别时,首先对放顶煤过程中采集到的顶煤和矸石冲击液压支架的振动信号进行集合经验模态分解分解(EEMD),得到若干个固有模态分量(IMF),根据分解结果提取包含振动信号主要信息的前8个IMF分量,进一步计算其能量矩,将待测样本信号的IMF能量矩作为特征向量输入训练好的支持向量机进行放煤和放矸石两种工况的识别。试验结果表明,该方法能有效的完成对煤矸振动样本数据的识别,平均识别准确率达到90%。

Abstract

To identify coal and gangue in a fully mechanized top-coal caving face, a coal-gangue identification approach based on IMF energy moment and SVM was proposed.Simulated signals used in the present research suggest that IMF energy moment, extracted from this approach, can effectively characterize the distribution of the signal energy along the time axis and better reflect signal characteristics in contrast with IMF energy.For data processing, firstly, the vibration signals generated by the impact of coal and gangue on the hydraulic support tail beam during top coal caving were collected; then these signals were decomposed into a series of intrinsic mode functions by the ensemble empirical mode decomposition; the first eight IMF components containing primary signal information were selected based on decomposing results to further extract the energy moment; each IMF energy moment was input into the trained support vector machine to identify two working conditions of coal caving and gangue caving.The experimental results show that this approach can effectively identify the coal-gangue vibration sample data and the average identification accuracy rate is up to 90%.

关键词

放顶煤 / 煤矸识别 / 集合经验模态分解(EEMD) / 固有模态函数(IMF) / 能量矩 / 支持向量机(SVM)

Key words

top coal caving / coal-gangue identification / ensemble empirical mode decomposition (EEMD) / intrinsic mode functions (IMF) / energy moment / support vector machine (SVM)

引用本文

导出引用
窦希杰1,2,王世博1,2,谢洋1,2,宣统1,2. 基于IMF能量矩和SVM的煤矸识别[J]. 振动与冲击, 2020, 39(24): 39-45
DOU Xijie1,2,WANG Shibo1,2,XIE Yang1,2,XUAN Tong1,2. Coal and gangue identification based on IMF energy moment and SVM[J]. Journal of Vibration and Shock, 2020, 39(24): 39-45

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