Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "T. Kolodziejczyk and R. Toscano and S. Fouvry and 
                 G. Morales-Espejel",
  title =        "Artificial intelligence as efficient technique for
                 ball bearing fretting wear damage prediction",
  journal =      "Wear",
  volume =       "268",
  number =       "1-2",
  pages =        "309--315",
  year =         "2010",
  ISSN =         "0043-1648",
  DOI =          "doi:10.1016/j.wear.2009.08.016",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Fretting,
                 Wear, Friction, Modelling, Artificial intelligence,
                 Artificial neural networks",
  abstract =     "Broadening functionality of artificial intelligence
                 and machine learning techniques shows that they are
                 very useful computational intelligence methods. In the
                 present study the potential of various artificial
                 intelligence techniques to predict and analyse the
                 damage is investigated. Pre-treated experimental data
                 was used to determine the wear of contacting surfaces
                 as a criterion of damage that can be useful for a
                 life-time prediction. The benefit of acquired knowledge
                 can be crucial for the industrial expert systems and
                 the scientific feature extraction that cannot be
                 underestimated. Wear is a very complex and partially
                 formalised phenomenon involving numerous parameters and
                 damage mechanisms. To correlate the working conditions
                 with the state of contacting bodies and to define
                 damage mechanisms different techniques are used. Neural
                 network structures are implemented to learn from
                 experimental data, genetic programming to find a
                 formula describing the wear volume and fuzzy inference
                 system to impose physically meaningful rules. To gain
                 data for the creation and verification of the model,
                 experiments were conducted on commonly used chromium
                 steel under dry and base oil bath-lubricated fretting
                 test apparatus. Decisive factors for a comparison of
                 used AI techniques are their: performance,
                 generalisation capabilities, complexity and
                 time-consumption. Optimisation of the structure of the
                 model is done to reach high robustness of field

Genetic Programming entries for Tomasz Kolodziejczyk Rosario Toscano Siegfried Fouvry Guillermo Morales-Espejel