Machine Fault Detection Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Samanta:2005:IDETC/CIE,
  author =       "B. Samanta",
  title =        "Machine Fault Detection Using Genetic Programming",
  booktitle =    "20th Biennial Conference on Mechanical Vibration and
                 Noise",
  year =         "2005",
  volume =       "1",
  pages =        "591--599",
  address =      "Long Beach, California, USA",
  month =        sep # " 24-28",
  publisher =    "ASME",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7918-4738-1",
  DOI =          "doi:10.1115/DETC2005-84642",
  abstract =     "Applications of genetic programming (GP) include many
                 areas. However applications of GP in the area of
                 machine condition monitoring and diagnostics is very
                 recent and yet to be fully exploited. In this paper, a
                 study is presented to show the performance of machine
                 fault detection using GP. The time domain vibration
                 signals of a rotating machine with normal and defective
                 gears are processed for feature extraction. The
                 extracted features from original and preprocessed
                 signals are used as inputs to GP for two class (normal
                 or fault) recognition. The number of features and the
                 features are automatically selected in GP maximising
                 the classification success. The results of fault
                 detection are compared with genetic algorithm (GA)
                 based artificial neural network (ANN)- termed here as
                 GA-ANN. The number of hidden nodes in the ANN and the
                 selection of input features are optimised using GAs.
                 Two different normalisation schemes for the features
                 have been used. For each trial, the GP and GA-ANN are
                 trained with a subset of the experimental data for
                 known machine conditions. The trained GP and GA-ANN are
                 tested using the remaining set of data. The procedure
                 is illustrated using the experimental vibration data of
                 a gearbox. The results compare the effectiveness of
                 both types of classifiers with GP and GA based
                 selection of features.",
  notes =        "Sultan Qaboos University, Muscat, Oman",
}

Genetic Programming entries for B Samanta

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