• DocumentCode
    175807
  • Title

    P-S-N curves with parameters estimated by particle swarm optimization and reliability prediction

  • Author

    Jinbao Zhang ; Ming Liu ; Yongqiang Zhao ; Xingguo Lu

  • Author_Institution
    Sch. of Mechatron. Eng., Harbin Inst. of Technol., Harbin, China
  • fYear
    2014
  • fDate
    19-21 Aug. 2014
  • Firstpage
    627
  • Lastpage
    631
  • Abstract
    The probabilistic characteristics of components can´t be completely expressed by the S-N curve with parameters estimated by small number of specimens. Particle Swarm Optimization (PSO) is introduced to fit parameters with the incomplete test data, which can take advantage of the entire information of the specimens to obtain the globally optimal solution. With the fitness function offered based on the principle of the total minimum mean-square value of fitting errors, the parameters of the three-parameter P-S-N curve are estimated with PSO. In sequence, the obtained P-S-N curve is applied in the fatigue damage accumulation model for reliability prediction. The above models are verified with test data with relation to two different 45 steels. The simulation results match well with experiment data.
  • Keywords
    fatigue; parameter estimation; particle swarm optimisation; probability; reliability theory; P-S-N curves; PSO; fatigue damage accumulation model; fitness function; fitting errors; parameter estimation; particle swarm optimization; probabilistic characteristics; reliability prediction; test data; total minimum mean-square value; Fatigue; Fitting; Loading; Mathematical model; Particle swarm optimization; Predictive models; Reliability; P-S-N curve; Particle Swarm Optimization; fitness function; parameter estimation; reliability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2014 10th International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4799-5150-5
  • Type

    conf

  • DOI
    10.1109/ICNC.2014.6975908
  • Filename
    6975908