Volume 48 Issue 9
Sep.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2022  >  48(9): 140-147, 156
XIANG Xueyi, LEI Zhipeng, LI Linbo, et al. Action recognition method for mine kilometer directional drilling rig[J]. Journal of Mine Automation，2022，48(9)：140-147, 156.  doi: 10.13272/j.issn.1671-251x.2022030103
Citation: XIANG Xueyi, LEI Zhipeng, LI Linbo, et al. Action recognition method for mine kilometer directional drilling rig[J]. Journal of Mine Automation，2022，48(9)：140-147, 156.  doi: 10.13272/j.issn.1671-251x.2022030103
shu

Action recognition method for mine kilometer directional drilling rig

doi: 10.13272/j.issn.1671-251x.2022030103
  • 1.

    National & Provincial Joint Engineering Laboratory of Mining Intelligent Electrical Apparatus Technology, Taiyuan University of Technology, Taiyuan 030024, China

  • 2.

    Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control, Taiyuan University of Technology, Taiyuan 030024, China

  • 3.

    Jinneng Holding Shanxi Science and Technology Research Institute Co., Ltd.(Jincheng) Technology Centre, Jincheng 048000, China

  • 4.

    Shanxi Jinding Gaobao Drilling Co., Ltd., Jincheng 048000, China

  • Received Date: 2022-03-31
  • Rev Recd Date: 2022-09-12
    • Available Online: 2022-07-07
    Abstract
  • At present, the walking and drilling operations of the mine kilometer directional drilling rig are all realized by the manual operation of drillers. The intelligence level is low. At present, there is no research on the correlation between the action type of kilometer directional drilling rig and the vibration state of the hydraulic pump station. Therefore, it is difficult to remotely identify the action type of the kilometer directional drilling rig. In order to solve the above problems, an action recognition method for mine kilometer directional drilling rig based on empirical wavelet transform (EWT) and fuzzy C-means (FCM) clustering algorithm is proposed. Firstly, the EWT method is used to analyze the frequency characteristic information of the three key parts (motor, hydraulic pump and coupling) of the hydraulic pump station when the kilometer directional drilling rig performs five different actions (the start of the kilometer directional drilling rig, the rotation of the power head without drill pipe, the rotation with drill pipe, the forward slow drilling with drill pipe and the forward fast drilling with drill pipe). The vibration signals in the most obvious direction of each vibration characteristic are selected to form the original signal group for action recognition. Secondly, the combination of EWT decomposition and correlation coefficient selection rules is used to extract eigenvectors containing drill action information in the original signal group for action recognition. The weight of different eigenvectors is confirmed. The standard recognition eigenvector is constructed. Finally, the membership degree between the action eigenvector to be identified and the five action recognition standard eigenvectors is obtained by using the FCM clustering algorithm. The intelligent recognition of the action types of the kilometer directional drilling rig is realized. Taking the ZYL-17000D type mine kilometer directional drilling rig as the research object, the reliability of the action recognition method based on EWT and FCM clustering algorithm for mine kilometer directional drilling rig is verified by experiments. The vibration data of the motor, hydraulic pump and coupling in the axial, horizontal and vertical radial directions under five actions are collected in the experiment. The results show that the empirical wavelet functions of the vibration signals of the motor, hydraulic pump and coupling of the drilling rig show different characteristics when it performs different actions. The clustering performance of the eigenvectors of the axial vibration signals of hydraulic pumps is the best. According to the difference of extracted eigenvectors under different actions, action types can be identified. The results of action recognition based on test data show that this method can effectively identify the action type of kilometer directional drilling rig, and the recognition accuracy is 96.8% when the membership degree is greater than 0.9.

     

    • FullText(HTML)
  • loading
    • References(16)
  • [1]
    王天龙,马斌,董洪波. 煤矿用自动化钻机远程监测系统研制[J]. 煤田地质与勘探,2022,50(1):80-85. doi: 10.12363/issn.1001-1986.21.12.0723

    WANG Tianlong,MA Bin,DONG Hongbo. Development of a remote monitoring system for coal mine automatic drilling rigs[J]. Coal Geology & Exploration,2022,50(1):80-85. doi: 10.12363/issn.1001-1986.21.12.0723
    [2]
    经哲,郭利. 基于广义相关系数自适应随机共振的液压泵振动信号预处理方法[J]. 振动与冲击,2016,35(16):72-78,85. doi: 10.13465/j.cnki.jvs.2016.16.013

    JING Zhe,GUO Li. Hydraulic pump vibration signal pretreatment based on adaptive stochastic resonance with a general correlation function[J]. Journal of Vibration and Shock,2016,35(16):72-78,85. doi: 10.13465/j.cnki.jvs.2016.16.013
    [3]
    李洪儒,王余奎,马济乔,等. 基于MMSE和ABCSVM的液压泵故障模式识别[J]. 振动与冲击,2016,35(9):152-158. doi: 10.13465/j.cnki.jvs.2016.09.024

    LI Hongru,WANG Yukui,MA Jiqiao,et al. Fault pattern recognition of hydraulic pumps based on MMSE and ABCSVM[J]. Journal of Vibration and Shock,2016,35(9):152-158. doi: 10.13465/j.cnki.jvs.2016.09.024
    [4]
    姜万录,李振宝,张生,等. 基于递归定量分析的液压泵故障识别方法[J]. 液压与气动,2019(2):18-23. doi: 10.11832/j.issn.1000-4858.2019.02.004

    JIANG Wanlu,LI Zhenbao,ZHANG Sheng,et al. Fault recognition method based on recurrent quantitation analysis for hydraulic pump[J]. Chinese Hydraulics & Pneumatics,2019(2):18-23. doi: 10.11832/j.issn.1000-4858.2019.02.004
    [5]
    郑直,李世峰,郭洋,等. 基于液压泵复数信号的log−SAM故障诊断方法研究[J]. 振动与冲击,2021,40(6):79-85. doi: 10.13465/j.cnki.jvs.2021.06.010

    ZHENG Zhi,LI Shifeng,GUO Yang,et al. Hydraulic pump fault diagnosis method using log-SAM on complex signals[J]. Journal of Vibration and Shock,2021,40(6):79-85. doi: 10.13465/j.cnki.jvs.2021.06.010
    [6]
    张幼振,刘焱杰,钟自成,等. 煤矿全液压动力头式钻机振动测试与分析[J]. 煤炭科学技术,2022,50(2):271-279. doi: 10.13199/j.cnki.cst.2021-1303

    ZHANG Youzhen,LIU Yanjie,ZHONG Zicheng,et al. Vibration measurement and analysis of full hydraulic power head drilling rig in coal mine[J]. Coal Science and Technology,2022,50(2):271-279. doi: 10.13199/j.cnki.cst.2021-1303
    [7]
    杜名喆,王宝中. 基于经验小波分解和卷积神经网络的液压泵故障诊断[J]. 液压与气动,2020(1):163-170. doi: 10.11832/j.issn.1000-4858.2020.01.027

    DU Mingzhe,WANG Baozhong. Fault diagnosis of hydraulic pump based on empirical wavelet transform and convolutional neural network[J]. Chinese Hydraulics & Pneumatics,2020(1):163-170. doi: 10.11832/j.issn.1000-4858.2020.01.027
    [8]
    HU Mantang, WANG Guofeng, MA Kaile, et al. Bearing performance degradation assessment based on optimized EWT and CNN[J]. Measurement, 2020, 172（1）. DOI: 10.1016/j.measurement.2020.108868.
    [9]
    LI Yuxing,JIAO Shangbin,GAO Xiang. A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy[J]. Defence Technology,2020,17(5):1-11.
    [10]
    赵妙颖,许刚. 基于经验小波变换的变压器振动信号特征提取[J]. 电力系统自动化,2017,41(20):63-69,91. doi: 10.7500/AEPS20170327001

    ZHAO Miaoying,XU Gang. Feature extraction for vibration signals of power transformer based on empirical wavelet transform[J]. Automation of Electric Power Systems,2017,41(20):63-69,91. doi: 10.7500/AEPS20170327001
    [11]
    LU Chuanqi,WANG Shaoping,ZHANG Chao. Fault diagnosis of hydraulic piston pumps based on a two-step EWFD method and fuzzy C-means clustering[J]. Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science,2016,230(16):203-210.
    [12]
    倪卫宁,张晓彬,万勇,等. 随钻方位电磁波电阻率测井仪分段组合线圈系设计[J]. 石油钻探技术,2017,45(2):115-120.

    NI Weining,ZHANG Xiaobin,WAN Yong,et al. The design of the coil system in LWD tools based on azimuthal electromagnetic-wave resistivity combined with sections[J]. Petroleum Drilling Techniques,2017,45(2):115-120.
    [13]
    PAN Yuna,CHEN Jin,LI Xinglin. Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy C-means[J]. Mechanical Systems and Signal Processing,2010,24:559-566. doi: 10.1016/j.ymssp.2009.07.012
    [14]
    尚海昆,苑津莎,王瑜,等. 基于交叉小波变换和相关系数矩阵的局部放电特征提取[J]. 电工技术学报,2014,29(4):274-281. doi: 10.3969/j.issn.1000-6753.2014.04.035

    SHANG Haikun,YUAN Jinsha,WANG Yu,et al. Feature extraction for partial discharge based on cross-wavelet transform and correlation coefficient matrix[J]. Transactions of China Electrotechnical Society,2014,29(4):274-281. doi: 10.3969/j.issn.1000-6753.2014.04.035
    [15]
    郑直,王宝中,刘佳鑫,等. 辛几何模态分解和广义形态分形维数的液压泵故障诊断[J]. 哈尔滨工程大学学报,2020,41(5):724-730.

    ZHENG Zhi,WANG Baozhong,LIU Jiaxin,et al. Hydraulic pump fault diagnosis method of symplectic geometry mode decomposition and generalized morphological fractal dimensions[J]. Journal of Harbin Engineering University,2020,41(5):724-730.
    [16]
    郭文琪,田慕琴,宋建成,等. 基于多源信号融合的离心泵叶轮磨损故障分析[J]. 工矿自动化,2018,44(6):74-79. doi: 10.13272/j.issn.1671-251x.2018020029

    GUO Wenqi,TIAN Muqin,SONG Jiancheng,et al. Wear fault analysis of centrifugal pump impeller based on multi-source signal fusion[J]. Industry and Mine Automation,2018,44(6):74-79. doi: 10.13272/j.issn.1671-251x.2018020029
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (275) PDF downloads(22) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return