Coal and gangue identification based on IMF energy moment and SVM

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (24) : 39-45.

DOU Xijie1,2,WANG Shibo1,2,XIE Yang1,2,XUAN Tong1,2

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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%.

Key words

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

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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

References

[1] 张守祥，张艳丽，王永强，等. 综采工作面煤矸频谱特[J]. 煤炭学报，2007, 32(9): 971-974.
ZHANG Shouxiang, ZHANG Yanli, WANG Yongqiang, et al. Caved coal and rock spectrum on longwall face [J]. Journal of the China Coal Society, 2007, 32(9): 971-974.
[2] 张宁波，鲁岩，刘长友，等. 综放开采煤矸自动识别基础研究[J]. 采矿与安全工程学报，2014, 31(4): 532-536.
ZHANG Ningbo, LU Yan, LIU Changyou, et al. Basic study on automatic detection of coal and gangue in the fully mechanized top coal caving mining [J]. Journal of Mining and Safety Engineering, 2014, 31(4): 532-536.
[3] 王保平，王增才，张万枝. 基于EMD与神经网络的煤岩界面识别方法[J]. 振动.测试与诊断，2012, 32(4): 66-70+168.
WANG Baoping, WANG Zengcai, ZHANG Wanzhi. Coal-Rock interface recognition method based on EMD and neural network [J]. Journal of Vibration Measurement and Diagnosis, 2012, 32(4): 66-70+168.
[4] 张宁波，刘长友，陈现辉，等. 综放煤矸低水平自然射线的涨落规律及测量识别分析[J]. 煤炭学报，2015, 40(5): 988-993.
ZHANG Ningbo, LIU Changyou, CHEN Xianhui, et al. Measurement analysis on the fluctuation characteristics of low level natural radiation from gangue [J]. Journal of China Coal Society, 2015, 40(5): 988-993.
[5] ZHANG N B, LIU C Y. Radiation characteristics of natural gamma-ray from coal and gangue for recognition in top coal caving [J]. Scientific Reports, 2018, 8(1): 190.
[6] 刘富强，钱建生，王新红，等. 基于图像处理与识别技术的煤矿矸石自动分选[J]. 煤炭学报，2000, 25(5): 534-537.
LIU Fuqiang, QIAN Jiansheng, WANG Xinhong, et al. Automatic separation of waste rock in coal mine based on image procession and recognition [J]. Journal of China Coal Society, 2000, 25(5): 534-537.
[7] 伍云霞，田一民. 基于字典学习的煤岩图像特征提取与识别方法[J]. 煤炭学报，2016, 41(12): 3190-3196.
WU Yunxia, TIAN Yimin. Method of coal-rock image feature extraction and recognition based on dictionary learning [J]. Journal of China Coal Society, 2016, 41(12): 3190-3196.
[8] SONG Q J, JIAN H Y, ZHAO X G, et al. An automatic decision approach to coal–rock recognition in top coal caving based on MF-Score [J]. Pattern Analysis and Applications, 2017, 20(4): 1307–1315.
[9] ZHANG G X, Wang Z C, Zhao L. Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders [J]. Journal of Vibroengineering, 2016, 18(7): 4261-4275.
[10] WANG B P, WANG Z C, LI Y X. Application of Wavelet Packet Energy Spectrum in Coal-rock Interface Recognition [J]. Key Engineering Materials, 2011, 474(476): 1103-1106.
[11] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceeding of the Royal Society London A, 1998, 454(1971): 903-995.
[12] HUANG N E. A new method for nonlinear and nonstationary time series analysis: empirical mode decomposition and Hilbert spectral analysis [J]. Proceedings of The International Society for Optical Engineering, 2000, 4056: 197-209.
[13] WU Z H, HUANG N E. Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method [J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[14] 徐卓飞，刘凯，张海燕，等. 基于经验模式分解和主元分析的滚动轴承故障诊断方法研究[J]. 振动与冲击，2014, 33(23): 141-147.
XU Zhuofei, LIU Kai, ZHANG Haiyan, et al. A fault diagnosis method for rolling bearings based on empirical mode decomposition and principal component analysis [J]. Journal of Vibration and Shock, 2014, 33(23): 141-147.
[15] 陈永高，钟振宇. 基于改进EEMD算法的桥梁结构响应信号模态分解研究[J]. 振动与冲击，2019, 38(10): 23-30.
CHEN Yonggao, ZHONG Zhenyu. Modal decomposition of response signals for a bridge structure based on the improved EEMD [J]. Journal of Vibration and Shock, 2019, 38(10): 23-30.
[16] 胡灿阳，陈清军. 基于EMD和最小二乘法的基线飘移研究[J]. 振动与冲击，2010, 29(3): 162-167.
HU Canyang, CHEN Qingjun. Research on baseline drift using least square and EMD [J]. Journal of Vibration and Shock, 2010, 29(3): 162-167.
[17] VAPNIK V N. The nature of statistical learning theory [M]. New York: Springer, 2000.
[18] CORTES C, VAPNIK V N. Support-vector networks [J]. Machine Learning, 1995, 20(3): 273-297.
[19] 徐玉秀，杨文平，吕轩，等. 基于支持向量机的汽车发动机故障诊断研究[J]. 振动与冲击，2013, 32(8): 143-146.
XU Yuxiu, YANG Wenping, LÜ Xuan, et al. Fault diagnosis for a car engine based on support vector machine[J]. Journal of Vibration and Shock, 2013, 32(8): 143-146.
[20] 李效莉. 基于支持向量机的文本分类方法研究[D]. 哈尔滨：哈尔滨工程大学，2015.
[21] WU Z H, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method [J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2004, 460(2046): 1597-1611.
[22] 杨宇，于德介，程军圣. 基于EMD与神经网络的滚动轴承故障诊断方法[J]. 振动与冲击，2005, 24(1): 85-88.
YANG Yu, YU Dejie, CHENG Junsheng. Roller bearing fault diagnosis method based on EMD and neural network [J]. Journal of Vibration and Shock, 2005, 24(1): 85-88.
[23] 朱启兵. 基于小波理论的非平稳信号特征提取与智能诊断方法研究[D]. 沈阳：东北大学，2006.
[24] VAPNIK V N, LEVIN E, CUN Y L. Measuring the VC-dimension of a learning machine [J]. Neural Computation, 1994, 6(5): 851-876.
[25] VAPNIK V N. The support vector method [M]. Berlin Heidelberg: Springer, 2006.