Fault Detection Based on Genetic Programming and Support Vector Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Liangmin Li and Liangsheng Qu",
  title =        "Fault Detection Based on Genetic Programming and
                 Support Vector Machines",
  journal =      "Journal of Xi'an Jiaotong University",
  year =         "2004",
  volume =       "38",
  number =       "3",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, fault
                 detection, support vector machines, SVM, rolling
  broken =       "http://unit.xjtu.edu.cn/xb/zrb/04/0403/xbe0405.html",
  URL =          "http://en.cnki.com.cn/Article_en/CJFDTotal-XAJT200403005.htm",
  abstract =     "A new classification model based on genetic
                 programming and support vector machine for machine
                 fault diagnosis was proposed.In this model,genetic
                 programming constructs and selects features from
                 original feature set.The selected features form input
                 feature set of support vector machines.Then multi-class
                 support vector machine is applied to detect abnormal
                 cases from normal ones.Experiments of rolling bearings
                 fault detection are carried out to test the performance
                 of this model.Practical results show that the compound
                 features generated by genetic programming possess
                 better recognition ability than the initial time domain
                 features do.The classification ability of multi-class
                 support vector machine is improved after feature
                 extraction and selection.",
  notes =        "http://unit.xjtu.edu.cn/xb/zrb/

                 School of Mechanical Engineering,Xi'an Jiaotong
                 University,Xi'an 710049,China",

Genetic Programming entries for Liangmin Li Liangsheng Qu