The design of acceptance sampling plans is developed under truncated life testing based on the percentiles of half normal distribution. The minimum sample size necessary to ensure the specified life percentile is obtained under a given consumer’s risk. The operating characteristic values of the sampling plans as well as the producer’s risk are presented. The results are illustrated by examples. 1. Introduction Acceptance sampling is concerned with inspection and decision making regarding lots of products and constitutes one of the oldest techniques in quality control. If the lifetime of the product represents the quality characteristics of interest, the acceptance sampling is as follows: a company receives a shipment of product from a vendor. This product is often a component or raw material used in the company’s manufacturing process. A sample is taken from the lot and the relevant quality characteristic of the units in the sample is inspected. On the basis of the information in the sample, a decision is made regarding lot disposition. Traditionally, when the life test indicates that the mean life of products exceeds the specified one, the lot of products is accepted, otherwise it is rejected. Accepted lots are put into production, while rejected lots may be returned to the vendor or may be subjected to some other lot disposition actions. For the purpose of reducing the test time and cost, a truncated life test may be conducted to determine the sample size to ensure a certain mean life of products when the life test is terminated at a time , and the number of failures does not exceed a given acceptance number . A common practice in life testing is to terminate the life test by a predetermined time and note the number of failures. One of the objectives of these experiments is to set a lower confidence limit on the mean life. It is then to establish a specified mean life with a given probability of at least which provides protection to consumers. The test may be terminated before the time is reached or when the number of failures exceeds the acceptance number in which case the decision is to reject the lot. Studies regarding truncated life tests can be found in Epstein [1], Sobel and Tischendrof [2], Goode and Kao [3], Gupta and Groll [4], Gupta [5], Fertig and Mann [6], Kantam and Rosaiah [7], Baklizi [8], Wu and Tsai [9], Rosaiah and Kantam [10], Rosaiah et al. [11], Tsai and Wu [12], Balakrishnan et al. [13], Srinivasa Rao et al. [14], Srinivasa Rao et al. [15], Aslam et al. [16], and Srinivasa Rao et al. [17]. All these authors designed acceptance
References
[1]
B. Epstein, “Truncated life tests in the exponential case,” Annals of Mathematical Statistics, vol. 25, pp. 555–564, 1954.
[2]
M. Sobel and J. A. Tischendrof, “Acceptance sampling with skew life test objective,” in Proceedings of the 5th National Syposium on Reliability and Qualilty Control, pp. 108–118, Philadelphia, Pa, USA, 1959.
[3]
H. P. Goode and J. H. K. Kao, “Sampling plans based on the Weibull distribution,” in Proceedings of the 7th National Syposium on Reliability and Qualilty Control, pp. 24–40, Philadelphia, Pa, USA, 1961.
[4]
S. S. Gupta and P. A. Groll, “Gamma distribution in acceptance sampling based on life tests,” Journal of the American Statistical Association, vol. 56, pp. 942–970, 1961.
[5]
S. S. Gupta, “Life test sampling plans for normal and lognormal distribution,” Technometrics, vol. 4, pp. 151–175, 1962.
[6]
F. W. Fertig and N. R. Mann, “Life-test sampling plans for two-parameter Weibull populations.,” Technometrics, vol. 22, pp. 165–177, 1980.
[7]
R. R. L. Kantam and K. Rosaiah, “Half Logistic distribution in acceptance sampling based on life tests,” IAPQR Transactions, vol. 23, no. 2, pp. 117–125, 1998.
[8]
A. Baklizi, “Acceptance sampling based on truncated life tests in the pareto distribution of the second kind,” Advances and Applications in Statistics, vol. 3, pp. 33–48, 2003.
[9]
C.-J. Wu and T.-R. Tsai, “Acceptance sampling plans for birnbaum-saunders distribution under truncated life tests,” International Journal of Reliability, Quality and Safety Engineering, vol. 12, no. 6, pp. 507–519, 2005.
[10]
K. Rosaiah and R. R. L. Kantam, “Acceptance sampling plans based on inverse Rayleigh distribution,” Economic Quality Control, vol. 20, no. 2, pp. 277–286, 2005.
[11]
K. Rosaiah, R. R. L. Kantam, and Ch. Santosh Kumar, “Reliability test plans for exponnetiated log-logistic distribution,” Economic Quality Control, vol. 21, pp. 165–175, 2006.
[12]
T.-R. Tsai and S.-J. Wu, “Acceptance sampling based on truncated life tests for generalized Rayleigh distribution,” Journal of Applied Statistics, vol. 33, no. 6, pp. 595–600, 2006.
[13]
N. Balakrishnan, V. Leiva, and J. López, “Acceptance sampling plans from truncated life tests based on the generalized Birnbaum-Saunders distribution,” Communications in Statistics: Simulation and Computation, vol. 36, no. 3, pp. 643–656, 2007.
[14]
G. Srinivasa Rao, M. E. Ghitany, and R. R. L. Kantam, “Reliability test plans for Marshall-Olkin extended exponential distribution,” Applied Mathematical Sciences, vol. 3, no. 53-56, pp. 2745–2755, 2009.
[15]
G. Srinivasa Rao, M. E. Ghitany, and R. R. L. Kantam, “An economic reliability test plan for Marshall-Olkin extended exponential distribution,” Applied Mathematical Sciences, vol. 5, no. 1–4, pp. 103–112, 2011.
[16]
M. Aslam, C.-H. Jun, and M. Ahmad, “A group sampling plan based on truncated life test for gamma distributed items,” Pakistan Journal of Statistics, vol. 25, no. 3, pp. 333–340, 2009.
[17]
G. Srinivasa Rao, M. E. Ghitany, and R. R. L. Kantam, “Reliability test plans for Marshall-Olkin extended exponential distribution,” Applied Mathematical Sciences, vol. 3, no. 53-56, pp. 2745–2755, 2009.
[18]
Y. L. Lio, T.-R. Tsai, and S.-J. Wu, “Acceptance sampling plans from truncated life tests based on the birnbaum-saunders distribution for percentiles,” Communications in Statistics: Simulation and Computation, vol. 39, no. 1, pp. 119–136, 2010.
[19]
G. Srinivasa Rao and R. R. L. Kantam, “Acceptance sampling plans from truncated life tests based on log-logistic distribution for percentiles,” Economic Quality Control, vol. 25, no. 2, pp. 153–167, 2010.
[20]
A. Wood, “Predicting software reliability,” IEEE Transactions on Software Engineering, vol. 22, pp. 69–77, 1996.
[21]
M. V. Aarset, “How to identify a bathtub hazard rate,” IEEE Transactions on Reliability, vol. 36, no. 1, pp. 106–108, 1987.