Weibull Parameter Estimation Using Particle Swarm Optimization Algorithm

  • Authors

    • Ekene Gabriel Okafor
    • Okechukwu Emmanuel Ezugwu
    • Paul Olugbeji Jemitola
    • Youchao Sun
    • Zhong Lu
    2018-08-26
    https://doi.org/10.14419/ijet.v7i3.32.18380
  • Algorithm, Parameter estimation, Particle Swarm Optimization, Reliability, Weibull distribution

  • Many research works on Weibull parameter estimation has focused on graphical or analytical techniques, with little effort devoted towards the use of population based optimization algorithm. Accurate estimation of failure distributive parameter such as Weibull is a key requirement for efficient reliability analysis. In this study Particle Swarm Optimization Algorithm (PSOA), with particle position and velocity iteratively updated was used to estimate Weibull parameters. Probability density function and reliability plots were generated using the results obtained. Generally, PSOA shows better parameter estimation in comparison with analytical method based on Maximum Likelihood Estimator (MLE).

     

     

  • References

    1. [1] Yang G (2007), Life Cycle Reliability Engineering†John Wiley & Sons.

      [2] Guure CB, & Ibrahim NA (2013), Method for Estimating the 2-Parameter Weibull Distribution with Type-I Censored Data, Research Journal of Applied Sciences, Engineering and Technology, Vol. 5 (3), pp. 689-694.

      [3] Nwobi FN & Ugomma CA (2014), A comparison of methods for the Estimation of Weibull distribution Parameter, Metodoloski Zvezki Vol 11(1) pp. 65-78.

      [4] Al-farwan MA (2000), Method for estimating the parameter of Weibull distribution, King Abdulaziz City for Science and Technology Saudi Arabia.

      [5] Lu Z, Sun Y-C, Wu H, Okafor EG & Zhou J (2010), Maintainability Parameter Estimation Model using Particle Swarm Optimization Based Nonlinear-Squares Method, Journal of Computational Information Sytem, 6(14), pp. 4665-4672.

      [6] Eberhart RC & Kennedy J (1995), A new optimizer using particle swarm theory [C], In Proceedings of the Sixth International Symposium on Micromachine and Human Science, pp: 39-43.

      [7] Lu Z & Sun Y-C (2009), Point Estimation Optimization Model of Life Distribution Parameters Based on Genetic Algorithm, In the Second International Conference on Information and Computing Science, pp. 169-172.

      [8] Moucun Y & Hong N (2007), Advanced Algorithm for Maximum Likelihood Estimation of Three Parameter Weibull Distribution, Journal of Nanjing University of Aeronautics &Astronautics, Vol. 39(1), pp. 22-25..

      [9] Jijun Z & Jinxian Z (2000), Estimation of Parameter Optimization f or Mixed Weibull Distributions from the Experimental Data of Reliability, Transactions of the Chinese Society for Agricultural Machinery, Vol. 31(5), pp. 106-109.

      [10] Lu H-L, Chen C-H & Wu J-W (2004), A Note on Weighted Least-Squares Estimation of the Shape Parameter of the Weibull Distribution, Quality and Reliability Engineering International, Vol. 20(6), pp. 579–586..

      [11] Nagode M & Fajdiga M (2000), An Improved Algorithm for Parameter Estimation Suitable for Mixed Weibull Distribution, International Journal of Fatigue, Vol. 22(1), pp. 75–80.

      [12] Saunders CS (2007), Reliability, Life Testing and Prediction of Service lives: For Engineers and Scientists, Springer, New York, pp. 307.

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  • How to Cite

    Gabriel Okafor, E., Emmanuel Ezugwu, O., Olugbeji Jemitola, P., Sun, Y., & Lu, Z. (2018). Weibull Parameter Estimation Using Particle Swarm Optimization Algorithm. International Journal of Engineering & Technology, 7(3.32), 7-10. https://doi.org/10.14419/ijet.v7i3.32.18380