Review Articles

Dynamic stress–strength reliability estimation of system with survival signature

Yiming Liu ,

Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, Shaanxi, People's Republic of China

scott_ymliu@163.com

Yimin Shi ,

Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, Shaanxi, People's Republic of China

Xuchao Bai ,

Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, Shaanxi, People's Republic of China

Bin Liu

Department of Applied Mathematics, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China

Pages 181-195 | Received 14 Mar. 2018, Accepted 28 Sep. 2018, Published online: 08 Oct. 2018,
  • Abstract
  • Full Article
  • References
  • Citations

ABSTRACT

In this paper, we proposed a dynamic stress–strength model for coherent system. It is supposed that the system consists of n components with initial random strength and each component is subjected to random stresses. The stresses, applied repeatedly at random cycle times, will cause the degradation of strength. In addition, the number of cycles in an interval is assumed to follow a Poisson distribution. In the case of the strength and stress random variables following exponential distributions, the expression for the reliability of the proposed dynamic stress–strength model is derived based on survival signature. The reliability is estimated by using the best linear unbiased estimation (BLUE). Considering the Type-II censored failure times, the best linear unbiased predictors (BLUP) for the unobserved coherent system failure times are developed based on the observed failure times. Monte Carlo simulations are performed to compare the BLUE of parameters with different values and compute the BLUP. A real data set is also analysed for an illustration of the findings.

References

  1. AL-Hussaini, E. K., Abdel-Hamid, A. H., & Hashem, A. F. (2015). Bayesian prediction intervals of order statistics based on progressively type-II censored competing risks data from the half-logistic distribution. Journal of the Egyptian Mathematical Society23(1), 190–196. doi: 10.1016/j.joems.2014.01.008 [Google Scholar]
  2. Arnold, B. C., Balakrishnan, N., & Nagaraja, H. N. (2008). A first course in order statistics. Siam: Society for Industrial and Applied Mathematics. [Google Scholar]
  3. Asgharzadeh, A., Valiollahi, R., & Kundu, D. (2015). Prediction for future failures in Weibull distribution under hybrid censoring. Journal of Statistical Computation and Simulation85(4), 824–838. doi: 10.1080/00949655.2013.848451 [Taylor & Francis Online][Web of Science ®], [Google Scholar]
  4. Aslett, L. J. M., Coolen, F., & Wilson, S. P. (2015). Bayesian inference for reliability of systems and networks using the survival signature. Risk Analysis35(9), 1640–1651. doi: 10.1111/risa.12228 [Google Scholar]
  5. Baklizi, A. (2014). Interval estimation of the stress–strength reliability in the two-parameter exponential distribution based on records. Journal of Statistical Computation and Simulation84(12), 2670–2679. doi: 10.1080/00949655.2013.816307 [Taylor & Francis Online], [Google Scholar]
  6. Balakrishnan, N., Ng, H. K. T., & Navarro, J. (2011). Linear inference for type-II censored lifetime data of reliability systems with known signatures. IEEE Transactions on Reliability60(2), 426–440. doi: 10.1109/TR.2011.2134371 [Web of Science ®], [Google Scholar]
  7. Basak, I. (2014). Prediction of times to failure of censored items for a simple step-stress model with regular and progressive type I censoring from the exponential distribution. Communications in Statistics – Theory and Methods43(10–12), 2322–2341. doi: 10.1080/03610926.2013.861489 [Taylor & Francis Online], [Google Scholar]
  8. Basak, I., & Balakrishnan, N. (2009). Predictors of failure times of censored units in progressively censored samples from normal distribution. Sankhyā: The Indian Journal of Statistics, Series B (2008-)71(2), 222–247. [Google Scholar]
  9. Basak, I., Basak, P., & Balakrishnan, N. (2006). On some predictors of times to failure of censored items in progressively censored samples. Computational Statistics & Data Analysis50(5), 1313–1337. doi: 10.1016/j.csda.2005.01.011 [Google Scholar]
  10. Bhuyan, P., & Dewanji, A. (2017a). Reliability computation under dynamic stress–strength modeling with cumulative stress and strength degradation. Communications in Statistics-Simulation and Computation46(4), 2701–2713. doi: 10.1080/03610918.2015.1057288 [Taylor & Francis Online], [Google Scholar]
  11. Bhuyan, P., & Dewanji, A. (2017b). Estimation of reliability with cumulative stress and strength degradation. Statistics51(4), 766–781. doi: 10.1080/02331888.2016.1277224 [Taylor & Francis Online], [Google Scholar]
  12. Bhuyan, P., Mitra, M., & Dewanji, A. (2016). Identifiability issues in dynamic stress–strength modeling. Annals of the Institute of Statistical Mathematics70(1), 63–81. doi: 10.1007/s10463-016-0579-4 [Google Scholar]
  13. Cha, J. H., & Finkelstein, M. (2015). A dynamic stress–strength model with stochastically decreasing strength. Metrika78(7), 807–827. doi: 10.1007/s00184-015-0528-x [Google Scholar]
  14. Chen, J., & Cheng, C. (2017). Reliability of stress–strength model for exponentiated Pareto distributions. Journal of Statistical Computation and Simulation87(4), 791–805. doi: 10.1080/00949655.2016.1226309 [Taylor & Francis Online], [Google Scholar]
  15. Coolen, F. P. A., & Coolen-Maturi, T. (2013). Generalizing the signature to systems with multiple types of components. Complex Systems and Dependability170, 115–130. doi: 10.1007/978-3-642-30662-4_8 [Google Scholar]
  16. Coolen, F. P. A., & Coolen-Maturi, T. (2016). On the structure function and survival signature for system reliability. Safety and Reliability36(2), 77–87. doi: 10.1080/09617353.2016.1219936 [Taylor & Francis Online], [Google Scholar]
  17. Coolen, F. P. A., Coolen-Maturi, T., & Al-nefaiee, A. H. (2013). Recent advances in system reliability using the survival signature. Proceedings advances in risk and reliability technology symposium (pp. 205–217). Loughborough. [Google Scholar]
  18. Dey, S., Mazucheli, J., & Anis, M. Z. (2017). Estimation of reliability of multicomponent stress–strength for a Kumaraswamy distribution. Communications in Statistics – Theory and Methods46(4), 1560–1572. doi: 10.1080/03610926.2015.1022457 [Taylor & Francis Online], [Google Scholar]
  19. Doganaksoy, N., & Balakrishnan, N. (1997). A useful property of best linear unbiased predictors with applications to life-testing. The American Statistician51(1), 22–28. [Taylor & Francis Online][Web of Science ®], [Google Scholar]
  20. Eryilmaz, S. (2013). On stress–strength reliability with a time-dependent strength. Journal of Quality and Reliability Engineering2013, 1–6. doi: 10.1155/2013/417818 [Google Scholar]
  21. Eryilmaz, S. (2014). Computing reliability indices of repairable systems via signature. Journal of Computational and Applied Mathematics260, 229–235. doi: 10.1016/j.cam.2013.09.023 [Google Scholar]
  22. Johnson, R. A. (1988). Stress–strength models for reliability. Handbook of Statistics7(88), 27–54. doi: 10.1016/S0169-7161(88)07005-1 [Google Scholar]
  23. Khan, A. H., & Jan, T. R. (2014). Estimation of multi component systems reliability in stress–strength models. Journal of Modern Applied Statistical Methods13(2), 389–398. doi: 10.22237/jmasm/1414815600 [Google Scholar]
  24. Kizilaslan, F., & Nadar, M. (2015). Classical and Bayesian estimation of reliability in multicomponent stress–strength model based on Weibull distribution. Revista Colombiana de Estadística38(2), 467–484. doi: 10.15446/rce.v38n2.51674 [Google Scholar]
  25. Kotz, S., Lumelskii, Y., & Pensky, M. (2003). The stress–strength model and its generalizations: Theory and applications. Singapore: World Scientific. [Google Scholar]
  26. Lawless, J. F. (2011). Statistical models and methods for lifetime data. Hoboken: John Wiley & Sons. [Google Scholar]
  27. Liu, Y., Shi, Y., & Bai, X. (2018). Reliability estimation of a N-M-cold-standby redundancy system in a multicomponent stress–strength model with generalized half-logistic distribution. Physica A: Statistical Mechanics and its Applications490, 231–249. doi: 10.1016/j.physa.2017.08.028 [Google Scholar]
  28. Mokhlis, N. A., Ibrahim, E. J., & Gharieb, D. M. (2017). Stress–strength reliability with general form distributions. Communications in Statistics – Theory and Methods46(3), 1230–1246. doi: 10.1080/03610926.2015.1014110 [Taylor & Francis Online], [Google Scholar]
  29. Pakdaman, Z., Ahmadi, J., & Doostparast, M. (2017). Signature-based approach for stress–strength systems. Statistical Papers, 1–17. doi: 10.1007/s00362-017-0889-5 [Google Scholar]
  30. Rao, G. S., Rosaiah, K., & Babu, M. S. (2016). Estimation of stress–strength reliability from exponentiated Fréchet distribution. The International Journal of Advanced Manufacturing Technology86(9–12), 3041–3049. doi: 10.1007/s00170-016-8404-z [Google Scholar]
  31. Sales Filho, R. L. M., López Droguett, E., & Lins, I. D. (2017). Stress–strength reliability analysis with extreme values based on q-exponential distribution. Quality and Reliability Engineering International33(3), 457–477. doi: 10.1002/qre.2020 [Google Scholar]
  32. Samaniego, F. J. (2007). System signatures and their applications in engineering reliability. Davis: Springer Science & Business Media. [Google Scholar]
  33. Siju, K. C., & Kumar, M. (2016). Reliability analysis of time dependent stress–strength model with random cycle times. Perspectives in Science8, 654–657. doi: 10.1016/j.pisc.2016.06.049 [Google Scholar]
  34. Wang, B. X., Geng, Y., & Zhou, J. X. (2018). Inference for the generalized exponential stress–strength model. Applied Mathematical Modelling53, 267–275. doi: 10.1016/j.apm.2017.09.012 [Google Scholar]