Equipment life prediction based on genetic algorithm under Weibull distribution

Authors

  • Khalid Talib Othman Iraqi University Author
  • Qusay Essam Hamid Iraqi University/ College of Education for Women Author

DOI:

https://doi.org/10.61841/r0pht836

Keywords:

Weibull distribution, genetic algorithm, life prediction

Abstract

This paper studies the problem of equipment reliability life prediction under the Weibull distribution. The improved genetic algorithm is mainly used to improve the genetic algorithm coding, objective function and genetic operation to realize the estimation of Weibull parameters. The Weibull distribution model is obtained and the reliability life model of the equipment.

 

 

Downloads

Download data is not yet available.

References

1. Zhang Xiaoli, Chen Xuefeng, etc. Overview of life prediction of major machinery equipment [J]. Journal of Mechanical Engineering, 2011, 47(9): 100–116.

2. Pan Dong, Zhao Yang, etc. Gear wear life prediction method [J]. Journal of Harbin Institute of Technology, 2012, 44(9): 29–33.

3. Xia Zhaopeng, Yu Jianyong. Studying the mechanical properties of jute/cotton blended yarns using the Weibull mode[J]. J. Donghua Univ., 2009, 4: 393–396.

4. Dragan Juki, Darija Markovi. On nonlinear weighted errors-in-variables parameter estimation prob- lem in the three- parameter Weibull model[J]. Appl. Math. Comput., 2010, 215: 3599–3609.

5. Tan Zhibin. A new approach to MLE of Weibull distribution with interval data[J]. Reliab. Engin. Sys. Saf., 2009, 94: 394–403.

6. Zhou Dan. Comparison of parameter estimation methods for transformer Weibull lifetime mod- elling[J]. High Voltage Technology, 2013, 39(5): 1170–1177.

7. Liu Shuxin, Liu Changwu, et al. Study on Weibull parameters of rock strength based on damage multifractal characteristics [J]. Chinese Journal of Geotechnical Engineering, 2011, 33(11): 1786–1791.

8. Wu Junjie. Research on genetic algorithm for optimization of hull assembly line drawing [J]. Journal of Dalian University of Technology, 2012, 52(3): 381–386.

9. Li Wei. Research on optimization of BP neural network based on rough set and improved genetic algorithm [J]. Journal of Northwestern Polytechnical University, 2012, 30(4): 601–606.

10. Liu Dalian, Xu Shangwen. Internal and external crossover genetic algorithm for solving constrained optimization problems [J]. Systems Engineering Theory and Practice, 2012, 32(1): 189–195.

11. Ren Ziwu, Umbrella Zhi. Improvement and performance research of real genetic algorithm [J]. Chinese Journal of Electronics, 2007, 35(2): 269–274.

12. Zhao Liang, Lu Jianhong. Multi-objective reactive power optimization of wind farms based on improved genetic algorithm [J]. Electric Power Automation Equipment, 2010, 30(10): 84–88.

13. Song Yisheng. The gradual evolution strategy of genetic algorithm based on shrinkage accuracy [J]. Journal of PLA University of Science and Technology, 2010, 11(3): 343–347.

14. Fan Qingwu, Wang Pu. The essential analysis of genetic algorithm crossover operator [J]. Journal of Beijing University of Technology, 2010, 36(10): 1328–1336.

15. Cui Jianguo, Zhao Yunlong. Life prediction of aviation generator based on genetic algorithm and ARMA model [J]. Acta Aeronautica Sinica, 2011, 32(8): 1506–1511.

16. Song Zhiqiang, Li Zhuxin. Analysis and assessment of fatigue life of buried oil and gas pipelines based on Weibull distribution [J]. Machine Tool & Hydraulics, 2011, 39(7): 130–133.

Downloads

Published

30.11.2020

How to Cite

Othman, K. T., & Hamid, Q. E. (2020). Equipment life prediction based on genetic algorithm under Weibull distribution. International Journal of Psychosocial Rehabilitation, 24(9), 5260-5267. https://doi.org/10.61841/r0pht836