Genetic programming for partial discharge feature construction in large generator diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Li Ruihua and Xie Hengkun and Gao Naikui and 
                 Shi Weixiang",
  title =        "Genetic programming for partial discharge feature
                 construction in large generator diagnosis",
  booktitle =    "Proceedings of the 7th International Conference on
                 Properties and Applications of Dielectric Materials",
  year =         "2003",
  volume =       "1",
  pages =        "258--261",
  month =        "1-5 " # jun,
  organisation = "IEEE",
  keywords =     "genetic algorithms, genetic programming, stator,
                 feature extraction, Artificial neural networks, Data
                 mining, Dielectrics and electrical insulation, Feature
                 extraction, Genomics, Partial discharges, Pattern
                 recognition, Stator windings, Thermal stresses,
                 electric generators, machine insulation, partial
                 discharges, generator diagnosis, partial discharge
                 defects, partial discharge feature construction",
  DOI =          "doi:10.1109/ICPADM.2003.1218401",
  abstract =     "In this paper, the standpoint of feature construction
                 is employed into partial discharge defects
                 identification of large generators by another emerging
                 simulated evolution technique- genetic programming
                 (GP). Genetic programming can discover relationships
                 among observed data and express them mathematically.
                 The architecture of partial discharge feature
                 construction is proposed. GP is applied to extract and
                 construct effective features from raw dataset. In
                 addition, in order to eliminate the bottleneck of
                 insufficient sample size, a kind of statistical
                 resampling technique called bootstrap is incorporated
                 as a preprocessing step into genetic programming. The
                 experimental results show the good ability in partial
                 discharge defects identification.",
  notes =        "real data on artificial defects. TE571",

Genetic Programming entries for Li Ruihua Xie Hengkun Gao Naikui Shi Weixiang