Genetic programming for partial discharge feature construction in large generator diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Ruihua:2003:PADM,

• 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

Citations