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1.中国矿业大学 机电工程学院,徐州 221116
2.江苏省矿山智能采掘装备协同创新中心,徐州 221116
3.智能采矿装备技术全国重点实验室,徐州 221116
吴明珂,男,2000年生,江苏徐州人,硕士研究生;主要研究方向为煤矸识别;E-mail:wumingke@cumt.edu.cn。
杨善国(通信作者),男,1970年生,安徽安庆人,博士,教授,硕士研究生导师;主要研究方向为智能矿山开采、声纹识别智能放煤、振动噪声分析与控制;E-mail:ysgcumt@163.com。
纸质出版日期:2025-01-15,
收稿日期:2024-05-16,
修回日期:2024-06-11,
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吴明珂, 杨善国, 王瑶, 等. 放顶煤液压支架尾梁动态特性数值与试验研究[J]. 机械强度, 2025,47(1):50-57.
WU MINGKE, YANG SHANGUO, WANG YAO, et al. Numerical and test study on dynamic characteristics of the tail beam of top coal caving hydraulic supports. [J]. Journal of mechanical strength, 2025, 47(1): 50-57.
吴明珂, 杨善国, 王瑶, 等. 放顶煤液压支架尾梁动态特性数值与试验研究[J]. 机械强度, 2025,47(1):50-57. DOI: 10.16579/j.issn.1001.9669.2025.01.006.
WU MINGKE, YANG SHANGUO, WANG YAO, et al. Numerical and test study on dynamic characteristics of the tail beam of top coal caving hydraulic supports. [J]. Journal of mechanical strength, 2025, 47(1): 50-57. DOI: 10.16579/j.issn.1001.9669.2025.01.006.
为确定放顶煤过程中煤、矸敏感振动特征参数,提高煤矸智能识别精度,研究液压支架尾梁的动态特性。首先,建立放顶煤液压支架与煤矸的刚柔耦合动力学模型,计算放煤过程中液压支架尾梁的振动加速度;其次,利用变分模态分解(Variational Mode Decomposition,VMD)对该加速度响应进行分解,得到固有模态函数(Intrinsic Mode Function,IMF),并分析每个IMF分量的时域和频域特征;然后,采用
t
分布式随机邻域嵌入(
t
-distributed Stochastic Neighborhood Embedding,
t
-SNE)方法对这些特征进行降维,并计算不同特征的平均轮廓系数(Average Silhouette Coefficient,ASC),对比研究了煤矸冲击下的尾梁振动特征;最后,搭建放顶煤液压支架试验台,对模型计算结果进行验证。结果表明,尾梁振动加速度IMF分量中的能量、奇异值、频率均值、峰值频率、谱心频率、频率方差对煤、矸特征较为敏感,可作为煤矸识别的特征参数。
In order to determine the sensitive vibration characteristic parameters of coal and gangue in the process of top coal caving and improve the intelligent identification accuracy of coal and gangue
the dynamic characteristics of the tail beam of the hydraulic support were studied. Firstly
the rigid-flexible coupling dynamic model of top coal caving hydraulic support and coal gangue was established
and the vibration acceleration of the tail beam of the hydraulic support in the process of coal caving was calculated. Secondly
the acceleration response was decomposed by variational mode decomposition (VMD) to obtain the intrinsic mode function (IMF)
and the time domain and frequency domain characteristics of each IMF component were analyzed. Thirdly
the
t
-distributed stochastic neighborhood embedding (
t
-SNE) method was used to reduce the dimension of these features
and the average silhouette coeffic
ient (ASC) of different features was calculated. The vibration characteristics of the tail beam under the impact of coal gangue were compared and studied. Finally
the bench of the top coal caving hydraulic support was built to verify the calculation results of the model. The results show that the energy
singular value
mean frequency
peak frequency
spectral centroid and frequency variance in the IMF component of the vibration acceleration of the tail beam are sensitive to the characteristics of coal and gangue
which can be used as the characteristic parameters of the coal and gangue identification.
放顶煤煤矸识别有限元法离散元法变分模态分解
Top coal cavingCoal gangue identificationFinite element methodDiscrete element methodVariational mode decomposition
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