A methodological approach ball bearing damage prediction under fretting wear conditions

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Tomasz Kolodziejczyk and Rosario Toscano and 
                 Cyril Fillon and Siegfried Fouvry and Carlo Poloni and 
                 Guillermo Morales-Espejel and Patrick Lyonnet",
  title =        "A methodological approach ball bearing damage
                 prediction under fretting wear conditions",
  booktitle =    "4th International IEEE Conference Intelligent Systems,
                 IS '08",
  year =         "2008",
  month =        sep,
  volume =       "2",
  pages =        "10--53--10--59",
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural network model, ball bearing damage prediction,
                 damage mechanisms, feature extraction, flywheels,
                 fretting wear conditions, mechanical engineering
                 computing, neural nets, optimisation, reliability,
  DOI =          "doi:10.1109/IS.2008.4670497",
  abstract =     "The industrial demand for higher reliability of
                 various components is one of the main flywheels of the
                 research and development in the field of modelling of
                 complex phenomena. There is a need to characterize the
                 wear behaviour of the interface under fretting wear
                 conditions in ball bearing application. 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-formalized 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. The use of our approaches in the prediction
                 of the response of the system to different test
                 conditions is validated. Two physical models, based on
                 Archard and Energetic approach, are compared with
                 artificial neural network model and genetic
                 programming. Decisive factors for a comparison of used
                 AI techniques are their: performance, generalization
                 capabilities, complexity and time-consumption.
                 Optimization of the structure of the model is done to
                 reach high robustness of field applications. Finally,
                 application of the wear level information to forecast a
                 probability of damage is presented.",
  notes =        "Also known as \cite{4670497}",

Genetic Programming entries for Tomasz Kolodziejczyk Rosario Toscano Cyril Fillon Siegfried Fouvry Carlo Poloni Guillermo Morales-Espejel Patrick Lyonnet