Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming

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

@Article{Ya:SV:06,
  title =        "Establishment of the mathematical model for diagnosing
                 the engine valve faults by genetic programming",
  author =       "Wen-Xian Yang",
  journal =      "Journal of Sound and Vibration",
  year =         "2006",
  volume =       "293",
  number =       "1-2",
  pages =        "213--226",
  month =        "30 " # may,
  keywords =     "genetic algorithms, genetic programming, Engine valve,
                 Fault diagnosis, Immigration operator",
  ISSN =         "0022-460X",
  bibsource =    "OAI-PMH server at dspace.lib.cranfield.ac.uk",
  identifier =   "Wen-Xian Yang, Establishment of the mathematical model
                 for diagnosing the engine valve faults by genetic
                 programming, Journal of Sound and Vibration, Volume
                 293, Issues 1-2, , 30 May 2006, Pages 213-226.;
                 0022-460X",
  language =     "en",
  oai =          "oai:dspace.lib.cranfield.ac.uk:1826/1131",
  URL =          "https://dspace.lib.cranfield.ac.uk/bitstream/1826/1131/1/Yang-JSVpaper-Math+model+engine+valve+faults.pdf",
  URL =          "http://hdl.handle.net/1826/1131",
  DOI =          "doi:10.1016/j.jsv.2005.09.004",
  abstract =     "Available machine fault diagnostic methods show
                 unsatisfactory performances on both on-line and
                 intelligent analyses because their operations involve
                 intensive calculations and are labour intensive. Aiming
                 at improving this situation, this paper describes the
                 development of an intelligent approach by using the
                 Genetic Programming (abbreviated as GP) method.
                 Attributed to the simple calculation of the
                 mathematical model being constructed, different kinds
                 of machine faults may be diagnosed correctly and
                 quickly. Moreover, human input is significantly reduced
                 in the process of fault diagnosis. The effectiveness of
                 the proposed strategy is validated by an illustrative
                 example, in which three kinds of valve states inherent
                 in a six-cylinders/four-stroke cycle diesel engine,
                 i.e. normal condition, valve-tappet clearance and gas
                 leakage faults, are identified. In the example, 22
                 mathematical functions have been specially designed and
                 8 easily obtained signal features are used to construct
                 the diagnostic model. Different from existing GPs, the
                 diagnostic tree used in the algorithm is constructed in
                 an intelligent way by applying a power-weight
                 coefficient to each feature. The power-weight
                 coefficients vary adaptively between 0 and 1 during the
                 evolutionary process. Moreover, different evolutionary
                 strategies are employed, respectively for selecting the
                 diagnostic features and functions, so that the
                 mathematical functions are sufficiently and in the
                 meantime, the repeated use of signal features may be
                 fully avoided. The experimental results are illustrated
                 diagrammatically in the following sections.",
}

Genetic Programming entries for Wenxian Yang

Citations