Reliability indicators for a parallel system for one or two decimal random data points
Abstract
The focus of the current work is on analysing component failure laws known as Weibull failure laws when evaluating reliability metrics like reliability and mean time to system failure (MTSF) of a parallel structure. Component failure rates, operation times, form parameters, and the overall number of components employed in the parallel structure have all been observed for one or two decimal random values. The specific case of the Weibull distribution has also been examined in order to examine the variation in reliability and MTSF values.
References
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27. Sarhan, A.M. (2000): Reliability Equivalence of Independent and Non-identical Components Series Systems, Reliability Engineering & System Safety, Vol. 67(3), pp.293-300.
2. Chauhan, S.K. and Malik, S.C. (2016): Reliability Measures of a Series System with Weibull Failure Laws, International Journal of Statistics and Systems, Vol. 11(2), pp. 173-186
3. Moskowitz, F. and Mclean, J.B. (1956): Some Reliability Accepts of system Design, IRE Transactions Reliability and Quality Control, Vol. 8, pp. 7-35.
4. Birnbaum, Z.W. and Saunders, S.C. (1958): A Statistical Model for Life-Length of Materials, Journal of American Statistical Society, Vol. 53, pp. 151-160.
5. Kao, J.H.K. (1958): Computer Methods for Estimating Weibull Parameters in Reliability Studies, IRE Trans. On Reliab. And Quality Control PGRQ, Vol. 13, pp. 15-22.
6. Kneale, S.G. (1961): Reliability of Parallel Systems with Repair and Switching, Proc. Seventh National Symposium on Reliability and Quality Control, pp. 129-133.
7. Sandler, G.I. (1963): System Reliability Engineering, Prentice Hall, Englewood Cliffs.
8. Basu, A.P. (1964): Estimates of Reliability for Some Distributions useful in Lift-Testing, Technometrics, Vol. 6, pp.215-219.
9. Berretoni, J.N. (1964): Practical applications of the Weibull Distribution, Ind. Qual. Control, Vol. 21, pp.71-79.
10. Barlow, R.E. and Prochan, P. (1965): Mathematical theory of Reliability, John Wiley, New York.
11. Shukla, D. K., & Arul, A. J. (2019): A smart component methodology for reliability analysis of dynamic systems. Annals of Nuclear Energy, 133, 863-880.
12. Wang, D., Jiang, C., & Park, C. (2019): Reliability analysis of load-sharing systems with memory. Lifetime Data Analysis, 25(2), 341-360.
13. Burton, R. M., & Howard, G. T. (2019): Optimal system reliability for a mixed series and parallel structure. Journal of Mathematical Analysis and Applications, 28(2), 370-382.
14. Granig, W., Faller, L. M., & Zang, H. (2018): Sensor system optimization to meet reliability targets. Microelectronics Reliability, 87, 113-124.
15. Xu, G., Du, X., Li, Z., Zhang, X., Zheng, M., Miao, Y. & Liu, Q. (2018): Reliability design of battery management system for power battery. Microelectronics Reliability, 88, 1286- 1292.
16. Abuta, E., & Tian, J. (2018): Reliability over consecutive releases of a semiconductor optical endpoint detection software system developed in a small company. Journal of Systems and Software, 137, 355-365.
17. Ivanov, V., Reznik, A., & Succi, G. (2018): Comparing the reliability of software systems: A case study on mobile operating systems. Information Sciences, 423, 398-411.
18. Utkin, L. V., & Coolen, F. P. (2018): A robust weighted SVR-based software reliability growth model. Reliability Engineering & System Safety, 176, 93-101.
19. Pundir, P. S., & Gupta, P. K. (2018): Reliability estimation in load-sharing system model with application to real data. Annals of Data Science, 5(1), 69-91.
20. Zhu, M. (2017): High reliability reconfigurable FOG signal processing system for satellite. Optik, 137, 25-30.
21. Ota, S., & Kimura, M. (2017): A statistical dependent failure detection method for n- component parallel systems. Reliability Engineering & System Safety, 167, 376-382.
22. Yazdanbakhsh, O., Dick, S., Reay, I., & Mace, E. (2016): On deterministic chaos in software reliability growth models. Applied Soft Computing, 49, 1256-1269.
23. Chauhan, S. K., & Malik, S. C. (2016): Reliability evaluation of series-parallel and parallel- series systems for arbitrary values of the parameters. International Journal of Statistics and Reliability Engineering, 3(1), 10-19.
24. Sagar, B. B., Saket, R. K., & Singh, C. G. (2016): Exponentiated Weibull distribution approach- based inflection S-shaped software reliability growth model. Ain Shams Engineering Journal, 7(3), 973-991.
25. Barraza, N. R. (2015): A parametric empirical bayes model to predict software reliability growth. Procedia Computer Science, 62, 360-369.
26. Kim, T., Lee, K., & Baik, J. (2015): An effective approach to estimating the parameters of software reliability growth models using a real-valued genetic algorithm. Journal of Systems and Software, 102, 134-144.
27. Sarhan, A.M. (2000): Reliability Equivalence of Independent and Non-identical Components Series Systems, Reliability Engineering & System Safety, Vol. 67(3), pp.293-300.
Published
2023-09-29
How to Cite
DUBEY, Niranjan.
Reliability indicators for a parallel system for one or two decimal random data points.
Journal of Advanced Research in Applied Mathematics and Statistics, [S.l.], v. 8, n. 1&2, p. 26-38, sep. 2023.
ISSN 2455-7021.
Available at: <http://thejournalshouse.com/index.php/Journal-Maths-Stats/article/view/856>. Date accessed: 30 dec. 2024.
Section
Review Article