Genetically programmed-based artificial features extraction applied to fault detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Hiram Firpi and George Vachtsevanos",
  title =        "Genetically programmed-based artificial features
                 extraction applied to fault detection",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "21",
  number =       "4",
  pages =        "558--568",
  year =         "2008",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2007.06.004",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Fault
                 detection, Feature extraction, Artificial feature,
                 Conventional feature",
  abstract =     "This paper presents a novel application of genetically
                 programmed artificial features, which are computer
                 crafted, data driven, and possibly without physical
                 interpretation, to the problem of fault detection.
                 Artificial features are extracted from vibration data
                 of an accelerometer sensor to monitor and detect a
                 crack fault or incipient failure seeded in an
                 intermediate gearbox of a helicopter's main
                 transmission. Classification accuracies for the
                 artificial feature constructed from raw data exceeded
                 99percent over training and independent validation
                 sets. As a benchmark, GP-based artificial features
                 constructed from conventional ones under performed
                 those derived from raw data by over 2percent over the
                 training and over 11percent over the testing data.",

Genetic Programming entries for Hiram A Firpi George Vachtsevanos