Fault Diagnosis of Drilling Process Based on Rough Set and Support Vector Machine

被引：2
|
作者：
Wang, Xiaoliang [1 ]
Lian, Xiaoyuan [1 ]
Yao, Lu [1 ]
机构：
[1] Dalian Univ Technol, Elect Informat & Elect Engn Dept, Dalian 116000, Peoples R China
来源：
ADVANCES IN APPLIED SCIENCE, ENGINEERING AND TECHNOLOGY | 2013年 / 709卷
关键词：
fault diagnosis; drilling process; rough set; support vector machine;
D O I：
10.4028/www.scientific.net/AMR.709.266
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Drilling process is a complicated system with characteristics of uncertainty, fuzziness and time-varying. A new way of the fault diagnosis based on RS-SVM (Rough Set and Support Vector Machine) was proposed in this paper. The related engineering factors were reduced by Rough Set theory and the main factors of the drilling process were obtained. Then the Support Vector Machine was used to establish the diagnosis models, and then the problems that the traditional SVM cannot deal with dynamic data and are prone to dimension disasters with large samples were avoided. The application in Ha35 well, Liao He Oilfield indicates that the system can diagnose the type of faults quickly and accurately. So the method can be used to diagnose the drilling process.
引用
收藏
页码：266 / 272
页数：7
相关论文
共 50 条
  • [1] Gear Fault Diagnosis Based on Rough Set and Support Vector Machine
    TIAN Huifang SUN Shanxia School of Mechanical and Electrical Engineering
    [J]. 武汉理工大学学报, 2006, (S3) : 1046 - 1051
  • [2] Gear fault diagnosis based on rough set and support vector machine
    Tian Huifang
    Sun Shanxia
    [J]. 1st International Symposium on Digital Manufacture, Vols 1-3, 2006, : 1046 - 1051
  • [3] Fault diagnosis system based on rough set theory and support vector machine
    Xu, YT
    Wang, LS
    [J]. FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 2, PROCEEDINGS, 2005, 3614 : 980 - 988
  • [4] Transformer Fault Diagnosis Utilizing Rough Set and Support Vector Machine
    Zang, Hongzhi
    Yu, Xiaodong
    [J]. 2009 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), VOLS 1-7, 2009, : 1270 - +
  • [5] Power transformer fault diagnosis based on rough set theory and support vector machine
    Zhao, Wenqing
    Zhu, Yongli
    [J]. FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 4, PROCEEDINGS, 2007, : 389 - 393
  • [6] Transformer Fault Diagnosis Based on rough sets and support vector machine
    Li Zhi-bin
    Xie Zhi-hui
    [J]. 2012 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2012,
  • [7] HVAC Fault Diagnosis System Using Rough Set Theory and Support Vector Machine
    Li Xuemei
    Shao Ming
    Ding Lixing
    [J]. WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2009, : 895 - +
  • [8] Power transformer fault diagnosis based on rough set theory and support vector machines
    Wu, Zhong-Li
    Yang, Jian
    Zhu, Yong-Li
    Liu, Zhu
    [J]. Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2010, 38 (18): : 80 - 83
  • [9] Fault diagnosis of FDM process based on support vector machine (SVM)
    Hu, Huaqing
    He, Ketai
    Zhong, Tianlin
    Hong, Yili
    [J]. RAPID PROTOTYPING JOURNAL, 2020, 26 (02) : 330 - 348
  • [10] Fault Diagnosis of a Hydro Turbine Generating Set Based on Support Vector Machine
    Yang, Chunting
    Tao, Jian
    Yu, Jing
    [J]. PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL III, 2009, : 415 - +
12345