Volume 48 Issue 12
Dec.  2022
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XU Zhipeng, LIU Zhenjian, ZHUANG Deyu, et al. Mining machine cutting load classification based on vibration signal[J]. Journal of Mine Automation，2022，48(12)：137-143. DOI: 10.13272/j.issn.1671-251x.2022070078
Citation: XU Zhipeng, LIU Zhenjian, ZHUANG Deyu, et al. Mining machine cutting load classification based on vibration signal[J]. Journal of Mine Automation，2022，48(12)：137-143. DOI: 10.13272/j.issn.1671-251x.2022070078
shu

Mining machine cutting load classification based on vibration signal

  • 1.

    China Coal Research Institute, Beijing 100013, China

  • 2.

    CCTEG Shanghai Research Institute, Shanghai 200030, China

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  • Received Date: July 27, 2022
  • Revised Date: December 05, 2022
  • Available Online: November 27, 2022
    Graphical Abstract
    • Abstract
  • There are some errors and lags in the way of judging the cutting load type of the mining machine manually. In order to solve the above problem, a classification method of mining machine cutting load based on wavelet packet decomposition and sparrow search algorithm optimized BP neural network (SSA-BPNN) is proposed. The method comprises two parts of signal feature extraction and mode classification. In the part of signal feature extraction, the collected vibration signal of the mining machine rocker arm is decomposed by wavelet packet to obtain the energy of each subband and the total energy of the signal. After normalization, feature vectors representing different load types are obtained. The principal component analysis is used to reduce the dimensions of the feature vector. In the mode classification part, SSA is used to optimize the initial weight and threshold of BPNN. The feature vector is used as the input of SSA-BPNN to realize the load classification and recognition. Taking the MG500/1170-AWD1 mining machine as an object, the magnetic acceleration sensor is attached to the shell of the rocker arm of the mining machine near the bracket side. The vibration signals of the mining machine drum under three working conditions of no-load, cutting bauxite and rock are collected and tested. The experimental results show that the vibration signals under different cutting loads have some differences in the energy of each sub-band. This result indicates that the energy features obtained by wavelet packet decomposition can be used as feature vectors to distinguish different load types. Compared with BPNN, SSA-BPNN has faster convergence speed and higher recognition accuracy, and the recognition accuracy of load classification is 95.3%.
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  • [1]
    张海坤,胡鹏,姜军胜,等. 铝土矿分布特点、主要类型与勘查开发现状[J]. 中国地质,2021,48(1):68-81. DOI: 10.12029/gc20210105

    ZHANG Haikun,HU Peng,JIANG Junsheng,et al. Distribution,genetic types and current situation of exploration and development of bauxite resources[J]. Geology in China,2021,48(1):68-81. DOI: 10.12029/gc20210105
    [2]
    展明鹏,马军强,姚强岭. 铝土矿综合机械化开采及应用研究[J]. 矿业研究与开发,2022,42(1):1-5. DOI: 10.13827/j.cnki.kyyk.2022.01.001

    ZHAN Mingpeng,MA Junqiang,YAO Qiangling. Comprehensive mechanized mining of bauxite and its application research[J]. Mining Research and Development,2022,42(1):1-5. DOI: 10.13827/j.cnki.kyyk.2022.01.001
    [3]
    张强,张润鑫,刘峻铭,等. 煤矿智能化开采煤岩识别技术综述[J]. 煤炭科学技术,2022,50(2):1-26. DOI: 10.13199/j.cnki.cst.2021-1333

    ZHANG Qiang,ZHANG Runxin,LIU Junming,et al. Review on coal and rock identification technology for intelligent mining in coal mines[J]. Coal Science and Technology,2022,50(2):1-26. DOI: 10.13199/j.cnki.cst.2021-1333
    [4]
    郭伟超,赵怀山,李成,等. 基于小波包能量谱与主成分分析的轴承故障特征增强诊断方法[J]. 兵工学报,2019,40(11):2370-2377. DOI: 10.3969/j.issn.1000-1093.2019.11.022

    GUO Weichao,ZHAO Huaishan,LI Cheng,et al. Fault feature enhancement method for rolling bearing fault diagnosis based on wavelet packet energy spectrum and principal component analysis[J]. Acta Armamentarii,2019,40(11):2370-2377. DOI: 10.3969/j.issn.1000-1093.2019.11.022
    [5]
    孙全,孙渊. 基于麻雀搜索算法的BP神经网络优化技术[J]. 上海电机学院学报,2022,25(1):12-16. DOI: 10.3969/j.issn.2095-0020.2022.01.003

    SUN Quan,SUN Yuan. Optimization technology of BP neural network based on sparrow search algorithm[J]. Journal of Shanghai Dianji University,2022,25(1):12-16. DOI: 10.3969/j.issn.2095-0020.2022.01.003
    [6]
    王冬云,张文志. 基于小波包变换的滚动轴承故障诊断[J]. 中国机械工程,2012,23(3):295-298.

    WANG Dongyun,ZHANG Wenzhi. Fault diagnosis study of ball bearing based on wavelet packet transform[J]. China Mechanical Engineering,2012,23(3):295-298.
    [7]
    鞠晨,张超,樊红卫,等. 基于小波包分解和PSO−BPNN的滚动轴承故障诊断[J]. 工矿自动化,2020,46(8):70-74. DOI: 10.13272/j.issn.1671-251x.2019120022

    JU Chen,ZHANG Chao,FAN Hongwei,et al. Rolling bearing fault diagnosis based on wavelet packet decomposition and PSO-BPNN[J]. Industry and Mine Automation,2020,46(8):70-74. DOI: 10.13272/j.issn.1671-251x.2019120022
    [8]
    葛渊博, 卢文喜, 白玉堃, 等. 基于SSA−BP与SSA的地下水污染源反演识别[J/OL]. 中国环境科学: 1-11[2022-07-21]. DOI: 10.19674/j.cnki.issn1000-6923.20220711.006.

    GE Yuanbo, LU Wenxi, BAI Yukun, et al. Inversion and identification of groundwater pollution sources based on SSA-BP and SSA[J/OL]. China Environmental Science: 1-11[2022-07-21]. DOI: 10.19674/j.cnki.issn1000-6923.20220711.006.
    [9]
    郭名诚,侯珂,李鑫浩. 基于SSA−BPNN模型的Web服务质量评价[J]. 信息技术与信息化,2021(10):13-15. DOI: 10.3969/j.issn.1672-9528.2021.10.003

    GUO Mingcheng,HOU Ke,LI Xinhao. Web service quality evaluation based on SSA-BPNN model[J]. Information Technology and Informatization,2021(10):13-15. DOI: 10.3969/j.issn.1672-9528.2021.10.003
    [10]
    陈晓玉,杜雅欣,刘亚茹,等. 三维荧光光谱结合2DPCA−SSA−GRNN对柴油占比的检测[J]. 中国激光,2022,49(18):175-182.

    CHEN Xiaoyu,DU Yaxin,LIU Yaru,et al. Detection of diesel proportion using three-dimensional fluorescence spectrum and 2DPCA-SSA-GRNN[J]. Chinese Journal of Lasers,2022,49(18):175-182.
    [11]
    刘湲,王芳. 麻雀搜索算法优化BP神经网络的短期风功率预测[J]. 上海电机学院学报,2022,25(3):132-136. DOI: 10.3969/j.issn.2095-0020.2022.03.002

    LIU Yuan,WANG Fang. BP neural networks optimized by sparrow search algorithm for short-term wind power prediction[J]. Journal of Shanghai Dianji University,2022,25(3):132-136. DOI: 10.3969/j.issn.2095-0020.2022.03.002
    [12]
    黄龙杨,夏正洪,贾鑫磊. 基于SSA−BP的离港航班滑出时间预测[J]. 科学技术与工程,2022,22(16):6607-6612.

    HUANG Longyang,XIA Zhenghong,JIA Xinlei. Departure flights' taxi-out time prediction based on SSA-BP algorithm[J]. Science Technology and Engineering,2022,22(16):6607-6612.
    [13]
    王耀国, 李勇永, 郭涛. 基于改进的SSA优化BP神经网络的导水断裂带高度预测[J/OL]. 煤矿安全: 1-8[2022-07-22]. DOI: 10.13347/j.cnki.mkaq.2023.02.001.

    WANG Yaoguo, LI Yongyong, GUO Tao. Prediction of height of water flowing fractured zone based on improved SSA to optimize BP neural network[J/OL]. Safety in Coal Mines: 1-8[2022-07-22]. DOI: 10.13347/j.cnki.mkaq.2023.02.001.
    [14]
    李福涛,王忠宾,司垒,等. 基于振动信号的采煤机煤岩截割状态识别[J]. 煤炭工程,2022,54(1):123-127.

    LI Futao,WANG Zhongbin,SI Lei,et al. Coal cutting state recognition of shearer based on vibration signal[J]. Coal Engineering,2022,54(1):123-127.
    [15]
    蒋干. 基于多传感信息融合的采煤机煤岩截割状态识别技术研究[D]. 徐州: 中国矿业大学, 2019.

    JIANG Gan. Research on recognition technology of shearer coal-rock cutting status based on multi-sensor information fusion[D]. Xuzhou: China University of Mining and Technology, 2019.
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