A New Approach to Improve System Reliability by Eliminating Early Failures

Document Type : Original Article

Authors

1 Assistant Professor, Department of Industrial Engineering, University of Gonabad.

2 Assistant Professor, Department of Industrial Engineering, University of Gonabad

Abstract

One of the key criteria for assessing the durability of a system is evaluating the probability of its performance over a specified time period, which is refered to as reliability. This probability depends on the performance of the system's components, and the failure rate of each component impacts this issue significantly. In most related studies, it is assumed that the failure rates of components remain constant over time; however, this rate is often not constant due to various factors. A common pattern for changes in failure rates over time is known as the bathtub curve, where the failure rate is initially high, decreases over a period, and then eventually increases again. The early life period, during which the failure rate of components is initially high and then decreases, can lead to early failures and a reduction in the probability of component performance. By conducting controlled experiments before the practical use of a system, this initial period can be eliminated, thereby preventing early failures, which in turn can lead to improved system reliability. This paper examines the impact of eliminating the early life period on system reliability. The results indicate that the elimination of this period does not necessarily lead to improved reliability under all conditions, as it depends on specific parameters. This paper will analyze the sensitivity of these parameters and the conditions under which eliminating the early life period can improve reliability.

Keywords

Main Subjects

References

Abunima, H., & Teh, J. (2020). Reliability modeling of PV systems based on time-varying failure rates. IEEE Access, 8, 14367-14376. https://doi.org/10.1109/ACCESS.2020.2966922
Attar, A., Raissi, S., Tohidi, H., & Feizollahi, M. J. (2023). A novel perspective on reliable system design with Erlang failures and realistic constraints for incomplete switching mechanisms. IEEE Access, 11, 51900-51914. https://doi.org/10.1109/ACCESS.2023.3280448
Bian, L., Wang, G., & Duan, F. (2021). Reliability analysis for systems subject to mutually dependent degradation and shock processes. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235(6), 1009-1025. https://doi.org/10.1177/1748006X211011448
Dong, W., Liu, S., Bae, S. J., & Cao, Y. (2021). Reliability modelling for multi-component systems subject to stochastic deterioration and generalized cumulative shock damages. Reliability Engineering & System Safety, 205, 107260. https://doi.org/10.1016/j.ress.2020.107260
Fang, G., Pan, R., & Hong, Y. (2020). Copula-based reliability analysis of degrading systems with dependent failures. Reliability Engineering & System Safety, 193, 106618. https://doi.org/10.1016/j.ress.2019.106618
Fan, K., Zhang, D., Li, G., & Hu, X. (2023). Reliability evaluation of distribution system based on time-varying failure rate model and non-homogeneous Poisson process. In 2023 3rd International Conference on Energy Engineering and Power Systems (EEPS) (pp. 593-597). IEEE. https://doi.org/10.1109/EEPS58791.2023.10257125
Gong, M., Eryilmaz, S., & Xie, M. (2020). Reliability assessment of system under a generalized cumulative shock model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(1), 129-137. https://doi.org/10.1177/1748006X19864831
Hu, Y., Ye, X., Zheng, B., Zhao, Z., & Zhai, G. (2022). Degradation mechanisms-based reliability modeling for metallized film capacitors under temperature and voltage stresses. Microelectronics Reliability, 138, 114609. https://doi.org/10.1016/j.microrel.2022.114609
Hsieh, T. J. (2021). Component mixing with a cold standby strategy for the redundancy allocation problem. Reliability Engineering & System Safety, 206, 107290. https://doi.org/10.1016/j.ress.2020.107290
Huang, T., Zhao, Y., Coit, D. W., & Tang, L. C. (2021). Reliability assessment and lifetime prediction of degradation processes considering recoverable shock damages. IISE Transactions, 53(5), 614-628. https://doi.org/10.1080/24725854.2020.1793036
Li, Y., Liu, Z., He, Z., Tu, L., & Huang, H. Z. (2023). Fatigue reliability analysis and assessment of offshore wind turbine blade adhesive bonding under the coupling effects of multiple environmental stresses. Reliability Engineering & System Safety, 238, 109426. https://doi.org/10.1016/j.ress.2023.109426
Liu, Y., Wang, Y., Fan, Z., Bai, G., & Chen, X. (2021). Reliability modeling and a statistical inference method of accelerated degradation testing with multiple stresses and dependent competing failure processes. Reliability Engineering & System Safety, 213, 107648. https://doi.org/10.1016/j.ress.2021.107648
Mousavi SA, Hamadani AZ, Gholinezhad H. Reliability Modeling for Degrading Systems Subject to Random Direct Shocks Transmitted Between Components. Quality and Reliability Engineering International. 2025. https://doi.org/10.1002/qre.70054
Peng, T., & You, Z. (2021). Reliability of MEMS in shock environments: 2000–2020. Micromachines, 12(11), 1275. https://doi.org/10.3390/mi12111275
Poursaeed, M. H. (2021). Reliability analysis of an extended shock model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235(5), 845-852. https://doi.org/10.1177/1748006X20987794
Reihaneh, M., Ardakan, M. A., & Eskandarpour, M. (2022). An exact algorithm for the redundancy allocation problem with heterogeneous components under the mixed redundancy strategy. European Journal of Operational Research, 297(3), 1112-1125. https://doi.org/10.1016/j.ejor.2021.06.033
Sadeghi, M., Roghanian, E., Shahriari, H., & Sadeghi, H. (2021). Reliability optimization for non-repairable series-parallel systems with a choice of redundancy strategies and heterogeneous components: Erlang time-to-failure distribution. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 235(3), 509-528. https://doi.org/10.1177/1748006X20952575
Sharifi, M., Pourkarim Guilani, P., Zaretalab, A., & Abhari, A. (2023). Reliability evaluation of a system with active redundancy strategy and load-sharing time-dependent failure rate components using Markov process. Communications in Statistics-Theory and Methods, 52(13), 4514-4533. https://doi.org/10.1080/03610926.2021.1995433
Sun, F., Li, H., Cheng, Y., & Liao, H. (2021). Reliability analysis for a system experiencing dependent degradation processes and random shocks based on a nonlinear Wiener process model. Reliability Engineering & System Safety, 215, 107906. https://doi.org/10.1016/j.ress.2021.107906
Wang, X., Zhao, X., Wang, S., & Sun, L. (2020). Reliability and maintenance for performance-balanced systems operating in a shock environment. Reliability Engineering & System Safety, 195, 106705. https://doi.org/10.1016/j.ress.2019.106705
Yeh, W. C., Zhu, W., Tan, S. Y., Wang, G. G., & Yeh, Y. H. (2022). Novel general active reliability redundancy allocation problems and algorithm. Reliability Engineering & System Safety, 218, 108167. https://doi.org/10.1016/j.ress.2021.108167
Zhang, D., Li, G., Bie, Z., & Fan, K. (2024). An analytical method for reliability evaluation of power distribution system with time-varying failure rates. Reliability Engineering & System Safety. Advance online publication. https://doi.org/10.1016/j.ress.2024.110290
Zhang, Z., Xu, D., & Wang, H. (2024). Degradation dynamics-based reliability assessment of unmanned ground vehicles. In 2024 10th International Symposium on System Security, Safety, and Reliability (ISSSR) (pp. 29-37). IEEE. https://doi.org/10.1109/ISSSR61934.2024.00009
Zhao, X., Chai, X., Sun, J., & Qiu, Q. (2021). Optimal bivariate mission abort policy for systems operating in random shock environments. Reliability Engineering & System Safety, 205, 107244. https://doi.org/10.1016/j.ress.2020.107244
Zio E, Gholinezhad H. Redundancy allocation of components with time-dependent failure rates. Mathematics. 2023 Aug 16;11(16):3534. https://doi.org/10.3390/math11163534.
 
Journal of Data Analytics and Intelligent Decision-making
Volume 1, Issue 2 - Serial Number 1
September 2025
Pages 1-12

Files

Share

How to cite

Statistics