Acceptance Sampling Plans for Truncated Life Tests Using the Marshall–Olkin Birnbaum–Saunders Distribution
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Noor S. Al-Momani
and
Abedel-Qader S. Al-Masri
Abstract
An acceptance sampling plan (AS) for a truncated life test is proposed based on the Marshall–Olkin Birnbaum–Saunders (MOBS) distribution. The minimum sample size required to guarantee the specified mean life is determined. Additionally, the operating characteristic function values and producer’s risk associated with the proposed sampling plans are presented. Tables summarizing the results are provided, supported by numerical examples to illustrate the findings. Finally, the proposed method is demonstrated through numerical examples and a real-life application.
Acknowledgements
The authors gratefully acknowledge the constructive feedback of the anonymous reviewers and appreciate the support and encouragement of colleagues that contributed to the development of this work.
References
[1] J. A. Achcar, Inference for the Birnbaum–Saunders distribution fatigue life model using Bayesian methods, Comput. Statist. Data Anal. 15 (1993), 367–380. 10.1016/0167-9473(93)90170-XSearch in Google Scholar
[2] B. Ahmed, M. M. Ali and H. M. Yousof, A novel G family for single acceptance sampling plan with application in quality and risk decisions, Ann. Data Sci. 11 (2024), 181–199. 10.1007/s40745-022-00451-3Search in Google Scholar
[3] A. I. Al-Omari and G. A. Alomani, Acceptance sampling plans based on percentiles for extended generalized exponential distribution with real data application, J. Radiation Res. Appl. Sci. 17 (2024), no. 4, Article ID 101081. 10.1016/j.jrras.2024.101081Search in Google Scholar
[4] A. Baklizi, A. S. Al-Masri and A. Al-Nasser, Acceptance sampling based on truncated life tests in the Rayleigh model, Korean Commun. Stat. 12 (2005), 11–18. 10.5351/CKSS.2005.12.1.011Search in Google Scholar
[5] N. Balakrishnan, V. Leiva, A. Sanhueza and F. Vilca, Estimation in the Birnbaum–Saunders distribution based on scale-mixtures of normals and the EM algorithm, SORT 33 (2009), no. 2, 171–192. Search in Google Scholar
[6] Z. W. Birnbaum and S. C. Saunders, A new family of life distributions, J. Appl. Probab. 6 (1969), no. 2, 319–327. 10.2307/3212003Search in Google Scholar
[7] M. E. Ghitany, F. A. Al-Awadhi and L. A. Al-Khalfan, Marshall–Olkin extended Lomax distribution and its application to censored data, Comm. Statist. Theory Methods 36 (2007), 1855–1866. 10.1080/03610920601126571Search in Google Scholar
[8] R. R. L. Kantam, K. Rosaiah and G. S. Rao, Acceptance sampling based on life tests: Log-logistic models, J. Appl. Stat. 28 (2001), 121–128. 10.1080/02664760120011644Search in Google Scholar
[9] A. S. Thefeid, A Quality Systems Economic–Risk Design Theoretical Framework, Old Dominion University, Norfolk, 2022. Search in Google Scholar
[10] H. Tripathi, M. Saha and S. Halder, Single acceptance sampling inspection plan based on transmuted Rayleigh distribution, Life Cycle Reliab. Saf. Eng. 12 (2023), no. 2, 111–123. 10.1007/s41872-023-00221-xSearch in Google Scholar
[11] A. Wood, Predicting software reliability, Computer 29 (1996), 69–77. 10.1109/2.544240Search in Google Scholar
[12] C. W. Wu, T. C. Wang and R. H. Chen, Development of acceptance sampling plans under time-truncated life test for verifying product reliability using Weibull-percentile lifetimes, Reliab. Eng. Syst. Saf. 261 (2025), Article ID 111062. 10.1016/j.ress.2025.111062Search in Google Scholar
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