Principal Components Analysis Based Sticking for Drill Rod

被引:0
|
作者
Li, Dongmin [1 ]
Xia, Shangfei [2 ]
Li, Jia [3 ]
机构
[1] Shandong Univ Sci & Technol, Coll Intelligent Equipment, Tai An, Shandong, Peoples R China
[2] Zaozhuang Tech Coll, Sch Enterprise Cooperat & Res Dept, Shandong, Peoples R China
[3] Jilin Univ, Sch Mech & Aerosp Engn, Changchun, Jilin, Peoples R China
关键词
sticking for drill rod; coal mine; analytic hierarchy process; torque; principal component analysis; COAL;
D O I
10.1007/978-981-96-0780-8_23
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It is a vital threat for coal mining to stick for drill rod during the process of drilling holes toward coal wall underground coal mines, which is prone to lead to mine accidents. To eliminate the above problem, all the factors inducing sticking for drill rod were studied. First, a complete evaluation system was built using analytic hierarchy process to reveal the interaction relationship among the factors inducing sticking for drill rod. Additionally, the characters of all the factors were analyzed and the key influencing factors were obtained using principal component analysis. Furthermore, the mechanical analysis on the drill rod was performed based on the key influencing factors, and the maximum stress region on the drill rod was obtained. Finally, the simulation results show their coincidence with the effectiveness of the key influencing factors, which proves the validity of the analysis approach on sticking for drill rod, thus the sticking for drill rod can be solved efficiently according to the analysis.
引用
收藏
页码:317 / 331
页数:15
相关论文
共 50 条
  • [31] Electricity Consumption Model Analysis based on Sparse Principal Components
    Yao, Bo
    Xu, Yiming
    Pang, Yue
    Jin, Chaoyi
    Tan, Zijing
    Zhou, Xiangdong
    Su, Yun
    PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS (ICPRAM 2018), 2018, : 590 - 596
  • [32] Detection of gross errors in DEM based on principal components analysis
    Yang, Xiaoyun
    Cen, Minyi
    Liang, Xin
    Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 2009, 44 (06): : 830 - 834
  • [33] Cluster Analysis of Autistic Patients Based on Principal Pathogenetic Components
    Sacco, Roberto
    Lenti, Carlo
    Saccani, Monica
    Curatolo, Paolo
    Manzi, Barbara
    Bravaccio, Carmela
    Persico, Antonio M.
    AUTISM RESEARCH, 2012, 5 (02) : 137 - 147
  • [34] Principal Components Analysis-Based Visual Saliency Detection
    Yang, Bing
    Zhang, Xiaoyun
    Liu, Jing
    Chen, Li
    Gao, Zhiyong
    2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 1936 - 1940
  • [35] A Threat Assessment Algorithm Based on AHP and Principal Components Analysis
    Yin, Gao-yang
    Zhou, Shao-lei
    Zhang, Wen-guang
    CEIS 2011, 2011, 15
  • [36] Properties of design-based functional principal components analysis
    Cardot, Herve
    Chaouch, Mohamed
    Goga, Camelia
    Labruere, Catherine
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2010, 140 (01) : 75 - 91
  • [37] Differences in Cutting Knee Mechanics Based on Principal Components Analysis
    O'Connor, Kristian M.
    Bottum, Michael C.
    MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 2009, 41 (04): : 867 - 878
  • [38] Quality control of semiconductor packaging based on principal components analysis
    He, Shuguang
    Qi, Ershi
    He, Zhen
    Nie, Bin
    Chinese Journal of Mechanical Engineering (English Edition), 2007, 20 (06): : 84 - 86
  • [39] Community detecting in bipartite network based on principal components analysis
    Liu, W., 1600, Asian Network for Scientific Information (13):
  • [40] Study on Inversion of Temperature Distribution Based on Principal Components Analysis
    Hu Xin-yue
    Gao Ming-xi
    Ren Yu
    Tan Jian-yao
    Li Wei
    Jin Kun
    Shi San-zhi
    Cai Hong-xing
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32 (10) : 2789 - 2793