Optimizing presetting attributes by softcomputing techniques to improve tapered roller bearings working conditions

  1. Fernandez Martinez, R. 2
  2. Lostado Lorza, R. 3
  3. Santos Delgado, A.A. 1
  4. Piedra Pullaguari, N.O. 1
  1. 1 Universidad Técnica Particular de Loja
    info

    Universidad Técnica Particular de Loja

    Loja, Ecuador

    ROR https://ror.org/04dvbth24

  2. 2 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

  3. 3 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Advances in Engineering Software

ISSN: 0965-9978

Año de publicación: 2018

Volumen: 123

Páginas: 13-24

Tipo: Artículo

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DOI: 10.1016/J.ADVENGSOFT.2018.05.005 SCOPUS: 2-s2.0-85047824126 GOOGLE SCHOLAR

Otras publicaciones en: Advances in Engineering Software

Objetivos de desarrollo sostenible

Resumen

Double-row Tapered Roller Bearings are mechanical devices that have been designed to support a combination of loads that are fixed on an optimal presetting to ensure correct working conditions. The emergence of high contact stresses, fatigue spalling and pitting on the bearing railway makes it important to have a tool that enables knowing in advance whether certain presetting loads will lead to excellent working conditions or the opposite. This work proposes a methodology to classify the working condition on the basis of the values of presenting loads on four classes. To achieve this goal, a three-dimensional Finite Element (FE) model was generated. Later, a design of experiments was designed to provide the greatest amount of information by reducing the computational cost of the simulations based on FE models. Then, one of the four classes of working conditions was assigned to each of the experiments. Later, a statistical analysis and machine learning techniques were used to create classification models. Feature transformation and reduction, algorithm parameter tuning and validation methods were used to achieve robust classification models. The best results were obtained based on flexible discriminant analysis. As it provided acceptable accuracy, both the methodology and final model were validated. © 2018 Elsevier Ltd